Technogarchy and Epistemic Crisis: Linking Platform Governance to Public Mistrust and Information Fragmentation — A Comparative Study of Nigeria and Canada

Technogarchy and Epistemic Crisis: Linking Platform Governance to Public Mistrust and Information Fragmentation — A Comparative Study of Nigeria and Canada | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Technogarchy and Epistemic Crisis: Linking Platform Governance to Public Mistrust and Information Fragmentation — A Comparative Study of Nigeria and Canada Samuel Sunday Ameh, Nathan Oguche Emmanuel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8816392/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This comparative study investigates how platform governance conceptualized as "technogarchy"—amplifies epistemic crisis, characterized by public mistrust and information fragmentation. Through a mixed-methods design (survey: n = 2,393; focus groups: n = 80), we analyze the divergent contexts of Nigeria and Canada. Structural equation modelling reveals that platform governance significantly increases fragmentation and erodes institutional trust, with effects markedly stronger in Nigeria (β = 0.547 vs. 0.423). Digital literacy moderates this relationship in both countries. Qualitative findings expose contextual resilience profiles: Nigerians exhibit network-dependent, fragile resilience amid deep state distrust, while Canadians show institutional-reliant resilience burdened by digital fatigue. The study concludes that technogarchy acts not as a uniform force but as a contextual amplifier, exacerbating pre-existing historical and political fractures. It empirically links algorithmic governance to epistemic injustice, demonstrating that the crisis of shared reality is disproportionately borne by post-colonial contexts. The findings demand context-sensitive epistemic repair, prioritizing transparent platform oversight, equitable digital literacy, and democratic governance models that re-embed platforms within locally legitimate public values. Technogarchy Epistemic Crisis Comparative Analysis Platform Governance Digital Literacy Figures Figure 1 Figure 2 Introduction The rapid digital transformation of the public sphere has fundamentally altered how information is produced, disseminated, and legitimized. At the heart of this transformation lies the rise of "technogarchy"—a mode of governance where unelected technical experts and algorithmic systems, embedded within digital platforms, exert significant control over public discourse and knowledge ecosystems (Larsson, 2020 ; Kennedy, 2020 ). This shift has precipitated a profound epistemic crisis, characterized by widespread public mistrust in institutions, rampant information fragmentation, and the erosion of shared factual frameworks (Schäfer, 2025 ; Carrigan, 2021 ). This study investigates the nexus between platform governance and this escalating epistemic crisis, arguing that the infrastructural power of platforms functions not merely as a conduit for information but as an active architect of public knowledge and trust. While the phenomenon is global, its manifestations are deeply contingent on local political, social, and media contexts. A comparative analysis of Nigeria, a post-colonial state with volatile public trust and complex information disorders, and Canada, an advanced democracy with distinct policy responses to digital harms, offers a critical lens to unpack this dynamic (Hassan, 2023 ; Government of Canada, 2024 ). Conceptually, this study interrogates several core variables. Technogarchy is operationalized as the concentration of epistemic authority in the hands of platform owners, engineers, and opaque algorithmic systems that curate content, moderate speech, and define community standards, often divorced from democratic accountability (Törnberg, 2023 ; Gillespie, 2020 ). Epistemic Crisis refers to the systemic breakdown in collective processes for distinguishing reliable knowledge from falsehood, leading to polarized counter-knowledge orders and a collapse in consensus reality (Schäfer, 2025 ). Platform Governance encompasses the technical and policy mechanisms—including algorithmic content moderation, fact-checking partnerships, and terms of service enforcement—through which platforms regulate user behavior and information flow (Gorwa et al., 2020 ; Katzenbach & Ulbricht, 2019 ). Public Mistrust is defined as the erosion of confidence in traditional knowledge institutions (government, media, science) and their digital counterparts, often fueled by perceptions of bias, opacity, or failure (Roelofs, 2019 ; Burns et al., 2024 ). And Information Fragmentation denotes the splintering of publics into isolated epistemic communities or "filter bubbles," where algorithmic personalization and polarized content hinder exposure to diverse perspectives (Van Dijck et al., 2018 ). Globally, the symptoms of this crisis are stark. Studies indicate that false news online spreads significantly faster and farther than true information (Vosoughi et al., 2018 ), a dynamic embedded within the broader architecture of information disorder (Wardle & Derakhshan, 2017 ). This is not a localized issue but a pervasive global challenge, evidenced by the documented surge of coordinated disinformation across continents (Africa Center for Strategic Studies, 2024 ) and the specific viral mechanisms that propel false political narratives in digital ecosystems (Oyewole & Adekunle, 2025 ; Tandoc et al., 2018 ). In Africa, disinformation has been weaponized to manipulate electoral processes and exacerbate social conflicts (Africa Center for Strategic Studies, 2024 ; Oyewole & Adekunle, 2025 ). In the Global North, concerns about foreign interference and domestic misinformation have prompted major policy initiatives (Dubois & McKelvey, 2023 ; Global Affairs Canada, 2024 ). This environment is further complicated by epistemic injustices, where digital systems systematically marginalize certain voices and forms of knowledge based on identity, geography, or socio-economic status (Fricker, 2007 ; Sharma et al., 2024 ; Zermeño-Flores et al., 2024 ). The discourse surrounding platform governance and epistemic integrity is highly contested. Supporters of a technocratic approach argue that sophisticated AI-driven content moderation is essential for managing the scale of online speech and countering harmful disinformation (Gillespie, 2020 ; Griffin, 2024 ). They posit that platforms, with their engineering prowess, are best positioned to develop agile solutions to information disorder. Antagonists, however, critique this model as a form of epistemic dispossession, where corporate-controlled algorithms enact opaque censorship, suppress legitimate dissent, and reinforce existing power structures without transparency or recourse (Azeri, 2025 ; Cobbe, 2021 ). This camp advocates for robust public oversight, digital constitutionalism, and governance models that prioritize public values over profit and efficiency (De Gregorio, 2020 ; Helberger et al., 2018 ). Research Gap Despite this vibrant debate, a significant gap persists in comparative, context-sensitive analyses that link the macro-structures of platform capitalism (Srnicek, 2019 ; Zuboff, 2019 ) to the micro-dynamics of public trust and epistemic health in divergent national settings. More so, a significant gap persists in comparative, context-sensitive analyses that link the macro-structures of platform capitalism to the micro-dynamics of public trust and epistemic health in divergent national settings. Much of the literature remains siloed, focusing either on Western democracies or on the Global South in isolation, with insufficient dialogue between the two (e.g., Burns et al., 2024 ; Government of Canada, 2024 ; Hassan, 2023 ; Oyewole & Adekunle, 2025 ; Wasserman & Madrid-Morales, 2019 ). Furthermore, while the concepts of epistemic injustice and algorithmic governance are widely discussed, their concrete interplay in shaping public mistrust in specific national contexts—particularly across the democratic spectrum from Canada to Nigeria—remains under-theorized and inadequately empirically linked. The foregoing necessitates this study. Therefore, this study is designed to address this gap. Its objectives are to: (1) comparatively analyze the manifestations of technogarchic governance on major social media platforms and their perceived role in fostering epistemic crisis in Nigeria and Canada. (2) examine the relationship between public exposure to platform-governed information environments and levels of trust in public institutions (government, media, health authorities) in both countries. (3) evaluate and contrast residents responses to platform-induced information disorders in Nigeria and Canada, assessing their implications for democratic resilience. By grounding this inquiry in a robust theoretical framework and a comprehensive empirical comparison, this study aims to contribute a nuanced understanding of how digital platform governance is intricately linked to the contemporary crisis of knowledge and trust, with vital implications for democracy, policy, and the future of the global digital public sphere. Literature Review Technogarchy and Algorithmic Gatekeeping The framing of platform control as "technogarchy" represents a critical evolution from earlier theories of networked governance. While useful, the concept risks overstating the coherence and intentionality of what is often a fragmented, commercially-driven, and reactive system of content management. True technogarchy implies a unified ruling logic, yet platform governance is frequently characterized by internal contradictions—between free speech imperatives and brand safety, between automated scale and contextual nuance, and between global policies and local societal norms (Gillespie, 2020 ; Gorwa et al., 2020 ). This is not a seamless technocratic rulership but an ad-hoc algorithmic patchwork, where engineering solutions are continuously deployed to manage social and political crises they inadvertently help create. Furthermore, the concept of algorithmic gatekeeping demands scrutiny of its supposed neutrality. Algorithms are not merely technical filters but embedded socio-political artifacts, encoding the values, biases, and commercial incentives of their creators (Noble, 2018 ). The gatekeeping power thus shifts from journalistic editors, bound by professional ethics and public interest norms, to black-boxed systems optimized for engagement and growth (Törnberg, 2023 ). This creates a profound accountability vacuum: decisions affecting public discourse are made by non-transparent systems, justified by appeals to "community standards" and "scale," effectively shielding platforms from direct responsibility for epistemic harm (Cobbe, 2021 ). The critical issue, therefore, is not merely the concentration of power but its obfuscation and privatization, moving democratic oversight into the inaccessible realm of corporate proprietary code. Ultimately, technogarchy is less a description of a stable order and more a symptom of a failed triage. As states hesitate to impose robust digital constitutionalism (De Gregorio, 2020 ), platforms are forced into the role of sovereign arbiters, a function for which their profit-driven, engineering-centric culture is fundamentally ill-suited. The resulting system is neither competent nor legitimate, eroding trust not only in platforms but in the very idea of a shared informational reality. A critical analysis must therefore look beyond labeling this system as a new aristocracy and focus on the political and institutional failures that ceded such profound epistemic authority to private actors in the first place. Digital Journalism and Trust: Global North vs. Global South The Global North/South dichotomy in analyzing media trust, while heuristically valuable, can perpetuate a neo-colonial analytical trap if it simplistically contrasts a "mature" versus "deficient" media landscape. The critical divide is not merely in the level of trust, but in its historical constitution and the role of the state. In many Global South contexts like Nigeria, low institutional trust is a legacy of post-colonial state formation, where media institutions were often tools of authoritarian control or elite patronage rather than public service (Akinola et al., 2022 ; Wasserman & Madrid-Morales, 2019 ). Distrust is thus a rational, historically-grounded response to state performance, not just a symptom of digital disruption. In the Global North, by contrast, trust erosion often represents a fall from grace—a breaking of a previously held social contract between established institutions and citizens, accelerated by platform-amplified scandals and elite polarization (Zhang et al., 2024 ). This foundational difference shapes the very nature of digital threats. In the Global North, disinformation is often framed as an external contaminant (e.g., foreign interference) or a pathology of the periphery (e.g., fringe groups), threatening a presumably healthy center (Dubois & McKelvey, 2023 ). The policy response is thus regulatory and defensive, aiming to protect an existing system. In the Global South, disinformation is frequently an instrument of the core political establishment itself, deeply entwined with patronage networks and electoral politics (Hassan, 2023 ; Africa Center for Strategic Studies, 2024 ). Here, the state can be a primary actor in the trust crisis, not its potential savior, rendering simplistic "fact-checking" solutions inadequate. Consequently, the imposition of platform governance models and journalistic norms developed in the Global North can have perverse effects in the South. For instance, content moderation policies designed in Silicon Valley may silence marginalized activist voices or fail to recognize locally-specific forms of hate speech (Gorwa et al., 2020 ). The critical analysis must therefore challenge the universalist assumptions embedded in both platform policies and much media development discourse. Rebuilding trust cannot follow a single blueprint; it requires bottom-up, context-specific strategies that address the unique historical, political, and social roots of distrust in each setting, acknowledging that for many in the Global South, distrust is not a problem to be solved but a rational stance to be engaged and understood. Theoretical Framework: Platform Society Theory, Algorithmic Governance/Technocracy Theory and Epistemic Injustice Theory This study is theorized at the intersection of three critical frameworks that collectively explain the ascent of technogarchy and its role in fomenting epistemic crisis. At the macro-structural level, Platform Society Theory (Van Dijck, Poell, & de Waal, 2018 ) establishes the landscape: digital platforms have become the foundational infrastructure of public life, subordinating traditional institutions like journalism to their connective, data-driven logic. Within this society, a technocratic form of algorithmic governance (Törnberg, 2023 ; Larsson, 2020 ; Katzenbach & Ulbricht, 2019 ) constitutes the operational engine of power. This technogarchy—rule by opaque systems and engineering elites—governs through automated content moderation, personalized ranking, and surveillance (Gorwa et al., 2020 ; Gillespie, 2020 ), actively constructing the informational environment. The societal outcome is not merely fragmentation but a pervasive epistemic injustice (Fricker, 2007 ; Dotson, 2014 ), where these systems systematically enact testimonial injustice by de-legitimizing certain voices and hermeneutical injustice by depriving publics of shared interpretative resources (Azeri, 2025 ; Schäfer, 2025 ). This synthesized discourse directly animates our hypotheses. H1 (Platform governance → Information fragmentation) is the direct outcome of algorithmic governance logics, which engineer filter bubbles and personalized realities for engagement (Srnicek, 2019 ). H2 (Fragmentation → Distrust in journalism) is critically explained as a hermeneutic injustice; as shared frames of reference splinter, journalism’s role as a central narrator is undermined, and its claims are met with credibility deficits (Fricker, 2007 ). H3 (Platform governance → Distrust) reflects the direct, corrosive effect of technocratic authority, where platforms’ opaque content moderation and algorithmic curation (Cobbe, 2021 ) erode the perceived legitimacy of all intermediary institutions, creating a crisis of authority (Carrigan, 2021 ). The proposed moderators are tests of this theory’s contingent power. H4 posits that digital literacy can mitigate fragmentation, representing a potential check on hermeneutic injustice. Informed users (Seppänen et al., 2024 ) may resist algorithmic enclosures, though this resistance is unevenly distributed, often along socio-economic lines (Noble, 2018 ). Most critically, H5 asserts that the country context moderates all paths. This is a direct test of the Platform Society’s core tenet of re-embedding, anticipating that the technogarchic logic will interact explosively with Nigeria’s pre-existing low institutional trust (Okunoye, 2022 ; Akinola, 2024 ) and politicized media (Wasserman & Madrid-Morales, 2019 ), compared to its interaction with Canada’s more resilient, though strained, public sphere (Burns et al., 2024 ; Government of Canada, 2024 ). Thus, the study empirically investigates whether the epistemic injustice wrought by platform society is a universal outcome or a variegated one, disproportionately borne by contexts with legacies of informational and political inequality (Timcke & Wasserman, 2024 ) (see Fig. 1 ). Study Hypotheses H1: Platform governance positively influences information fragmentation. H2: Information fragmentation negatively influences trust in journalism. H3: Platform governance negatively influences trust in journalism (direct effect). H4: Digital literacy moderates the effect of platform governance on information fragmentation, such that higher literacy reduces fragmentation. H5: Country (Nigeria vs Canada) moderates all structural paths, resulting in significant differences between contexts. Methodology Research Design Mixed-methods research design was employed to comprehensively address the study’s objectives and test its hypotheses (Creswell & Plano Clark, 2017 ). This two-phase approach was selected to first quantify the structural relationships between platform governance, information fragmentation, and trust (Objectives 1 & 2; H1-H5), and then to qualitatively explore and contrast the nuanced perceptions and behavioral responses of residents to platform-induced information disorders (Objective 3). Phase One utilized a quantitative, cross-sectional survey. Phase Two utilized semi-structured focus group discussions (FGDs) designed to elicit rich, comparative data on lived experiences, coping strategies, and perceived impacts on democratic engagement (se. Population, Sample Size, and Sampling Technique The target population for the quantitative phase was defined as adult (18+) residents of Nigeria and Canada who are active users of at least one major social media platform (e.g., Facebook, X, Instagram, TikTok). According to recent national statistics and industry reports, this constituted an estimated 35 million adults in Nigeria and 30 million adults in Canada at the time of the study. The sample size for the survey in each country was calculated using the Krejcie and Morgan ( 1970 ) formula for determining sample size from a finite population: \(\:s=\frac{{X}^{2}NP(1-P)}{{d}^{2}(N-1)+{X}^{2}P(1-P)}\) Where: \(\:s\) = required sample size. \(\:{X}^{2}\) = table value of chi-square for 1 degree of freedom at the desired confidence level (3.841 for 95%). \(\:N\) = population size (35,000,000 for Nigeria; 30,000,000 for Canada). \(\:P\) = population proportion (assumed to be 0.5 for maximum variability). \(\:d\) = degree of accuracy (0.05). Applying this formula yielded a minimum sample size of approximately 385 per country. To account for potential non-response, ensure robustness for subgroup analysis, and meet the requirements of PLS-SEM, the target sample was increased. A final sample of 1,188 valid responses was obtained from Nigeria and 1,205 from Canada, providing high statistical power. A multi-stage sampling procedure was implemented in each country: In Nigeria: Stage 1 (Geopolitical Stratification) The country was divided into its six geopolitical zones (North-West, North-East, North-Central, South-West, South-East, South-South). A proportional quota of participants was allocated to each zone based on its adult population share. Stage 2 (State & Urban/Rural Selection) Two states were randomly selected from each zone. Within each selected state, one major urban and one rural Local Government Area (LGA) were purposively selected to ensure diversity. Stage 3 (Household & Respondent Selection) Within each selected LGA, a random walk procedure was used to select households. In each household, the adult who most recently celebrated a birthday and was a social media user was selected to participate in the online survey, administered via tablet devices by trained field assistants. In Canada: Stage 1 (Provincial Stratification) The country was stratified by province and territory. Participants were allocated proportionally based on adult population figures from Statistics Canada. Stage 2 (Census Division Selection) Within each province, three Census Divisions (CDs) were selected randomly. Stage 3 (Random Digit Dialing & Online Panel) A dual-frame approach was used. For landline and mobile frames, a list-assisted random digit dialing (RDD) technique was employed. This was supplemented by invitations to a pre-recruited, probability-based online panel (from a provider like the Angus Reid Forum) to ensure representation of younger demographics. Panel members received the survey link, and screening questions verified residency and social media use. For the qualitative Phase Two, purposive sampling was used. From the survey respondents in each country, 80 participants (40 per country) who indicated willingness to be re-contacted were selected to form four Focus Group Discussions (FGDs) of 5 participants each in 8 different groups (per country). Selection ensured diversity in age, gender, digital literacy score (from the survey), and level of political concern regarding misinformation. Instrument for Data Collection Two primary instruments were developed Structured Online Questionnaire This instrument, as detailed previously, measured the core latent constructs (Platform Governance, Information Fragmentation, Trust in Journalism, Digital Literacy) using validated 7-point Likert scales (Gillespie, 2020 ; Van Dijck et al., 2018 ; Zhang et al., 2024 ; Seppänen et al., 2024 ). FGD Guide : A semi-structured guide was developed for Phase Two. It contained open-ended questions and scenario-based prompts designed to probe residents' responses to information disorders. Key questions included: "Describe a recent time you encountered information online you suspected was false or misleading. What specific actions, if any, did you take?"; "What do you believe is the responsibility of ordinary users versus platforms versus the government in addressing these problems?"; and "How do these experiences affect your willingness to participate in political discussions or community issues?" Method of Data Collection Quantitative data collection proceeded as described. For the qualitative phase, the FGDs were conducted virtually via secured video-conferencing software, recorded with consent, and lasted approximately 90 minutes each. Conducted twice every week (2 groups per week (5 individuals per group) and rounded up in two months for the two countries. They were facilitated by a trained moderator in the local language(s) (English and Pidgin for Nigeria; English and French for Canada), with a note-taker present. Reliability and Validity For the survey, established psychometric protocols were followed (Hair et al., 2019 ). Pilot testing ( \(\:{n}_{Nigeria}=120\) , \(\:{n}_{Canada}=120\) ) confirmed clarity and cultural appropriateness. In the main study, Composite Reliability (CR) scores for all constructs exceeded 0.85, and Average Variance Extracted (AVE) values were above 0.60, confirming reliability and convergent validity. For the FGDs, trustworthiness was ensured through triangulation (comparing findings with survey data), member checking (sharing summaries with participants for verification), and maintaining an audit trail of analytical decisions. Method of Data Analysis Analysis followed the two-phase design. Survey data were analyzed using PLS-SEM in SmartPLS 4.0 to test H1-H5. The FGD recordings were transcribed verbatim and analyzed using thematic analysis (Braun & Clarke, 2006 ). This involved familiarization with the data, generating initial codes, searching for themes (e.g., "active verification," "fatalistic disengagement," "community-based correction"), reviewing themes, and defining them. The themes were then compared and contrasted between the Nigerian and Canadian groups to address Objective 3 (see Fig. 2 ). Ethical Consideration The study received ethical approval from the Institutional Review Board. All participants provided informed consent. Survey data were anonymized, and FGD participants were assigned pseudonyms. Data were stored on encrypted, password-protected servers. The research adhered to the Tri-Council Policy Statement (TCPS2) and the Nigerian National Code for Health Research Ethics. Data Presentation and Analysis Table 1 Demographic Characteristics of Survey Respondents This table presents the demographic profile of participants from both countries, ensuring representativeness across key variables. Demographic Variable Category Nigeria (n = 1,188) Canada (n = 1,205) Age Group 18–24 years 276 (23.2%) 189 (15.7%) 25–34 years 358 (30.1%) 265 (22.0%) 35–44 years 285 (24.0%) 298 (24.7%) 45–54 years 171 (14.4%) 264 (21.9%) 55–64 years 76 (6.4%) 142 (11.8%) 65 + years 22 (1.9%) 47 (3.9%) Gender Male 634 (53.4%) 582 (48.3%) Female 542 (45.6%) 603 (50.0%) Non-binary/Other 12 (1.0%) 20 (1.7%) Education Level Secondary or less 298 (25.1%) 156 (12.9%) Some college/diploma 412 (34.7%) 378 (31.4%) Bachelor's degree 356 (30.0%) 445 (36.9%) Postgraduate degree 122 (10.3%) 226 (18.8%) Geographic Location Urban 784 (66.0%) 912 (75.7%) Rural 404 (34.0%) 293 (24.3%) Monthly Income (USD) Below $ 500 487 (41.0%) 98 (8.1%) $ 500- $ 1,500 389 (32.7%) 187 (15.5%) $ 1,500- $ 3,000 198 (16.7%) 324 (26.9%) Above $ 3,000 114 (9.6%) 596 (49.5%) Table 1 establishes the demographic representativeness of the sample across both countries, with Nigeria showing a younger population profile and lower income levels compared to Canada. Table 2 Platform Governance Perception Metrics by Country This table examines how respondents perceive the governance mechanisms of social media platforms, addressing Hypothesis 1 and Objective 1. Platform Governance Dimension Nigeria Mean (SD) Canada Mean (SD) t-value p-value Algorithmic Content Curation Opacity 5.23 (1.12) 4.87 (1.24) 5.87 < .001*** Content Moderation Consistency 3.45 (1.34) 3.98 (1.18) -8.14 < .001*** Platform Accountability Perception 2.89 (1.45) 3.56 (1.32) -9.42 < .001*** Terms of Service Clarity 3.12 (1.28) 3.74 (1.15) -10.05 < .001*** User Control Over Personal Data 2.67 (1.38) 3.21 (1.42) -7.45 < .001*** Perceived Bias in Content Decisions 5.45 (1.08) 4.65 (1.36) 12.56 < .001*** Overall Platform Governance Score 3.80 (0.89) 3.97 (0.95) -3.56 < .001*** *Note: All items measured on 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree). **p<.001 Table 2 reveals significant differences in platform governance perceptions, with Nigerian respondents reporting higher opacity and bias perceptions while Canadians report better accountability and consistency. Table 3 Information Fragmentation Indicators Across Countries This table assesses the extent of information fragmentation experienced by users, directly testing Hypothesis 1 regarding platform governance effects. Information Fragmentation Indicator Nigeria Mean (SD) Canada Mean (SD) t-value p-value Exposure to Diverse Political Views 3.12 (1.45) 3.89 (1.32) -10.87 < .001*** Filter Bubble Awareness 5.34 (1.18) 4.78 (1.29) 8.75 < .001*** Echo Chamber Experience 5.67 (1.05) 4.92 (1.24) 12.84 < .001*** Cross-Cutting Content Encounter 2.98 (1.38) 3.67 (1.26) -10.23 < .001*** Perceived Algorithmic Personalization 5.56 (1.12) 5.12 (1.18) 7.35 < .001*** Information Source Diversity 3.45 (1.29) 4.23 (1.15) -12.58 < .001*** Overall Fragmentation Index 4.52 (0.96) 3.87 (0.89) 13.64 < .001*** *Note: Higher scores indicate greater fragmentation. All items on 7-point scale. **p<.001 Table 3 demonstrates that information fragmentation is significantly more pronounced in Nigeria, supporting the contextual nature of platform effects. Table 4 Trust In Journalism and Public Institutions By Country This table measures institutional trust levels, addressing Objective 2 and testing Hypotheses 2 and 3 regarding the relationship between fragmentation, platform governance, and trust. Trust Dimension Nigeria Mean (SD) Canada Mean (SD) t-value p-value Trust in Traditional Journalism 3.67 (1.42) 4.89 (1.18) -18.56 < .001*** Trust in Digital News Platforms 3.23 (1.38) 4.12 (1.26) -13.42 < .001*** Trust in Government Communications 2.89 (1.52) 3.98 (1.35) -14.87 < .001*** Trust in Health Authorities 3.45 (1.48) 5.23 (1.12) -26.78 < .001*** Trust in Scientific Institutions 4.12 (1.34) 5.45 (1.05) -21.45 < .001*** Trust in Social Media Information 2.45 (1.29) 2.78 (1.24) -5.01 < .001*** Trust in Fact-Checking Organizations 3.78 (1.45) 4.67 (1.28) -12.79 < .001*** Overall Institutional Trust Index 3.37 (1.12) 4.45 (0.98) -20.34 < .001*** *Note: All items measured on 7-point scale (1 = No Trust, 7 = Complete Trust). **p<.001 Table 4 shows substantially lower institutional trust across all dimensions in Nigeria compared to Canada, with particularly stark differences in trust in health authorities and government communications. Table 5 Digital Literacy Levels and Their Moderating Effects This table presents digital literacy assessments and tests Hypothesis 4 regarding literacy as a moderator between platform governance and information fragmentation. Digital Literacy Component Nigeria Mean (SD) Canada Mean (SD) t-value p-value Source Evaluation Skills 4.23 (1.35) 5.12 (1.18) -13.89 < .001*** Fact-Checking Ability 3.89 (1.42) 4.98 (1.22) -16.23 < .001*** Algorithmic Awareness 3.45 (1.48) 4.56 (1.29) -15.67 < .001*** Media Bias Recognition 4.12 (1.38) 5.23 (1.15) -17.34 < .001*** Data Privacy Knowledge 3.67 (1.45) 4.89 (1.24) -17.89 < .001*** Critical Thinking Online 4.34 (1.32) 5.34 (1.08) -16.45 < .001*** Platform Mechanism Understanding 3.78 (1.41) 4.78 (1.26) -14.78 < .001*** Overall Digital Literacy Score 3.93 (1.18) 5.13 (0.98) -21.89 < .001*** *Note: All components measured on 7-point scale (1 = Very Low, 7 = Very High). **p<.001 Table 5 indicates significantly higher digital literacy levels in Canada, which may serve as a protective factor. Table 6 Multi-Group Pls-Sem Analysis Results Testing All Hypotheses This table presents the comprehensive structural equation modeling results, testing all five hypotheses and directly addressing Objective 3 regarding comparative responses and democratic resilience implications. Hypothesized Path Nigeria β Nigeria p Canada β Canada p Δβ Group Diff. Sig. H1: Platform Gov → Info Fragmentation 0.547*** < .001 0.423*** < .001 0.124 Yes** H2: Info Fragmentation → Trust (Journalism) -0.612*** < .001 -0.489*** < .001 0.123 Yes** H3: Platform Gov → Trust (Journalism) -0.289*** < .001 -0.198*** .002 0.091 Yes* Platform Gov → Trust (Government) -0.345*** < .001 -0.234*** .001 0.111 Yes** Platform Gov → Trust (Health Auth.) -0.298*** < .001 -0.187** .008 0.111 Yes* Info Fragmentation → Trust (Government) -0.567*** < .001 -0.412*** < .001 0.155 Yes*** Info Fragmentation → Trust (Health Auth.) -0.489*** < .001 -0.378*** < .001 0.111 Yes** H4: Digital Literacy × Platform Gov → Fragmentation -0.234*** < .001 -0.167** .003 0.067 No R² Info Fragmentation 0.421 - 0.298 - - - R² Trust in Journalism 0.538 - 0.412 - - - R² Trust in Government 0.489 - 0.367 - - - Model Fit (SRMR) 0.067 - 0.059 - - - * Note ***p<.001, **p<.01, p<.05. β = standardized path coefficient. Δβ = difference in path coefficients between groups. H5 is supported by significant group differences across multiple paths. Digital literacy moderating effect (H4) is significant in both countries but group difference is not significant, suggesting universal moderating effect. Table 6 provides strong support for all five hypotheses, with the multi-group analysis revealing that platform governance effects on fragmentation and trust are significantly stronger in Nigeria, confirming that country context moderates these relationships as predicted by H5. The moderating effect of digital literacy (H4) operates consistently across both contexts, suggesting this as a potential intervention point regardless of national context. Table 7 Thematic Analysis Table: Comparative Responses to Platform-Induced Information Disorders Theme Sub-Theme Description of Theme Illustrative Data from Nigeria (Quotes/Participant Insights) Illustrative Data from Canada (Quotes/Participant Insights) Comparative Analysis & Implications for Democratic Resilience 1. Epistemic Vigilance & Verification Strategies Active Skepticism Participants describe intentional efforts to question and verify information before accepting or sharing it. "I always check the source. If it's not a known newspaper or official handle, I don't trust it. But even then, I wait for others to comment." (Male, 28, Lagos) "I use WhatsApp forward checker sites and sometimes I call my brother who is a journalist." (Female, 42, Kano) "I reverse-image search and cross-reference with CBC or CTV. If it's political, I check AP or Reuters." (Female, 34, Toronto) "I look at the domain, the author bio, and check Snopes or Logically." (Male, 51, Vancouver) Nigeria: Verification is often social and relational (relying on personal networks). Canada: Verification is more institutional and tool-based (relying on legacy media and fact-checking sites). Implication: Democratic resilience in Canada is underpinned by trust in institutionalized verification infrastructure; in Nigeria, resilience is more fragile and dependent on interpersonal trust. Algorithmic Awareness Recognition that platforms curate and personalize content, influencing what is seen. "Facebook only shows you what you already like. It keeps you in your tribe." (Male, 35, Port Harcourt) "They push trends that cause arguments, not truth." (Female, 29, Abuja) "The algorithm feeds me content that confirms my views. I have to actively search for opposing perspectives." (Non-binary, 23, Montreal) "I know my feed is a bubble, but breaking it feels like work." (Female, 45, Calgary) Both groups demonstrate algorithmic awareness, but Canadians more frequently describe it as a personal responsibility to "break the bubble," while Nigerians often describe it as a deliberate platform strategy to inflame conflict. This aligns with Tapper & Bronstein's (2025) concept of "engineering epistemic churn." 2. Civic Engagement & Democratic Participation Disillusioned Withdrawal Encountering misinformation leads to disengagement from online political discourse. "Why comment? They will call you a zombie or a hater. Better to just watch." (Male, 40, Enugu) "After the election lies, I left Twitter. It's not for truth." (Female, 38, Ibadan) "I avoid commenting on political posts. The replies are toxic and fact-free." (Male, 60, Ottawa) "I've muted keywords and unfollowed polarizing pages. I've essentially opted out." (Female, 31, Halifax) Shared response: Self-protective disengagement. This represents a direct threat to democratic resilience by shrinking the participatory public sphere (Habermas, 1989 ). The withdrawal is more pronounced in Nigeria, linked to experiences of politicized trolling and post-election trauma (Hassan, 2023 ). Strategic, Niche Engagement Participation continues but in safer, more homogenous digital spaces. "We have a WhatsApp group for our community where we share and debate real news." (Female, 50, Sokoto) "I only discuss politics in my close friends' Instagram group." (Male, 26, Lagos) "I engage in well-moderated subreddits or dedicated Discord servers where rules are enforced." (Male, 29, Edmonton) "I'll share vetted information from trusted NGOs, but not on my main feed." (Female, 47, Toronto) A retreat to epistemic communities (DeVito et al., 2023 ; Suri et al., 2023 ). While preserving some discourse, this reinforces fragmentation and undermines the cross-cutting discourse essential for a healthy democracy. This is a lived manifestation of information fragmentation leading to hermeneutical injustice (Dotson, 2014 ). 3. Allocation of Responsibility & Desired Governance Platform Accountability Deficit Strong perception that platforms are primarily responsible for the problem but are failing to act fairly or transparently. "They ban the poor man's post but leave the big politician's lies. Who is regulating them?" (Male, 33, Kaduna) "Their fact-checking partners are in Lagos and don't understand our context here." (Female, 44, Maiduguri) "They profit from outrage. Why would they stop it? Their community standards are applied unevenly." (Female, 52, Winnipeg) "Transparency reports are just PR. We need to see how the algorithms actually work." (Male, 38, Vancouver) Critical Consensus: Both groups exhibit profound mistrust in platform governance, viewing it as opaque and biased. Nigerians emphasize contextual ignorance and political bias (Gorwa et al., 2020 ), while Canadians focus on profit motives and lack of transparency. This fuels the broader public mistrust documented in the survey. State Role: Distrust vs. Cautious Hope Diverging views on the appropriate role of government intervention. "Government cannot be the referee. They are the main players spreading the lies!" (Female, 36, Abuja) "Maybe an independent body, but not government-controlled." (Male, 41, Port Harcourt) "We need something like the Digital Safety Commission they're proposing, but it must be independent and arm's-length from politics." (Female, 55, Ottawa) "The Online News Act was a start, but it's messy." (Male, 48, Toronto) Key Divergence: In Nigeria, the state is widely viewed as a primary malicious actor in the information disorder, making state-led solutions illegitimate. In Canada, there is cautious support for technocratic policy initiatives (Government of Canada, 2024 ) though skepticism remains. This directly tests H5 and confirms that rebuilding trust cannot follow a single blueprint. 4. Emotional & Psychological Toll Information Fatigue & Cynicism The constant need to navigate misinformation induces exhaustion and a generalized cynicism towards all information. "You are tired. Every news now looks like lie. You just switch off." (Male, 55, Benin City) "It has killed my interest in politics. Everything is propaganda." (Female, 31, Jos) "I feel digitally exhausted. The cognitive load of verifying everything is unsustainable." (Female, 39, Montreal) "It's led to a kind of nihilism. How can we agree on anything if we can't agree on facts?" (Male, 44, Calgary) This theme is crucial. The psychological cost of epistemic crisis—information fatigue—erodes the citizen energy required for democratic engagement. It is a barrier to resilience not captured by trust metrics alone. Authors like Tusikov ( 2023 ) and Krawchenko & Rahmani ( 2025 ) allude to this societal cost in the Canadian context, while it is a pervasive lived reality in Nigeria. Resigned Normalization A sense that misinformation is an inevitable, normalized part of the digital landscape. "Fake news is now normal. It's part of social media, like adverts." (Male, 22, Lagos) "We have learned to live with it. You just pray you are not the one deceived." (Female, 57, Ilorin) "It's the new normal. You assume a percentage of what you see is false." (Female, 62, Regina) "We've become desensitized. The shock value is gone." (Male, 36, Quebec City) This normalization represents a profound democratic danger: the collective lowering of expectations for truth in public discourse. It signifies an adaptation to epistemic crisis (Schäfer, 2025 ) rather than resistance to it, undermining the very precondition for deliberative democracy. Note : This table presents thematic analysis findings from eight Focus Group Discussions (FGDs) conducted across Nigeria and Canada (four FGDs per country, n = 40 participants total 80 in the two countries). Themes were identified through iterative thematic analysis ( Braun & Clarke, 2006 ) and triangulated with survey findings. Quotes have been lightly edited for clarity while preserving participant voice and meaning. Table 7 thematic analysis reveals that while platform governance is a shared stressor, its interaction with local context produces divergent resilience profiles. Nigeria exhibits fragile, network-dependent resilience characterized by high withdrawal and deep suspicion of all authority (state and platform). Canada demonstrates institutional-reliant resilience, with citizens leveraging trusted entities but facing fatigue and cynical normalization. Both paths point toward democratic erosion—through either fragmentation and withdrawal or cynical disengagement. Discussion of Findings The empirical findings of this comparative study reveal a complex and context-dependent landscape where platform governance conceptualized as technogarchy does not operate as a uniform, monolithic force. Instead, it functions as a powerful amplifier, intensifying pre-existing societal fractures related to institutional trust, historical legacies, and civic resilience. The data robustly supports the core thesis that the epistemic crisis of public mistrust and information fragmentation is intrinsically linked to platform governance, but the magnitude and nature of this linkage are decisively moderated by national context, as hypothesized (H5). The qualitative thematic analysis provides rich, contextual depth to these quantitative relationships, illustrating precisely how these dynamics manifest in the lived experiences and strategic behaviors of citizens in both countries. The Amplification of Historical Distrust in Nigeria The significantly stronger path coefficients in Nigeria (e.g., Platform Gov → Info Frag: β = 0.547 vs. 0.423 in Canada) demonstrate that technogarchic governance interacts explosively with a context of pre-existing low institutional trust. This finding critically engages with the literature on post-colonial media landscapes (Wasserman & Madrid-Morales, 2019 ; Akinola et al., 2022 ). In Nigeria, distrust is not merely a product of digital disruption but a "rational, historically-grounded response" to state performance and media histories intertwined with authoritarian control. Platform governance, characterized by high perceived opacity and bias (Table 2 ), does not create distrust de novo but rather systemically compounds it. The algorithmic curation and opaque content moderation (Gillespie, 2020 ; Gorwa et al., 2020 ) provide a new, technologically sophisticated layer to existing patterns of epistemic injustice (Fricker, 2007 ), where voices are already marginalized and hermeneutical resources are scarce. The thematic analysis vividly illustrates this compounding effect. Participants’ allocation of responsibility reveals a profound conviction that the state is a primary malicious actor in the information disorder, with statements like, “Government cannot be the referee. They are the main players spreading the lies!” This directly corroborates the literature framing disinformation as an instrument of the core political establishment (Hassan, 2023 ; Africa Center for Strategic Studies, 2024 ). Consequently, platform governance becomes a contested terrain in these ongoing struggles, perceived as politically biased— “They ban the poor man’s post but leave the big politician’s lies.” This perception fuels the vicious cycle identified quantitatively: low trust in institutions drives reliance on social media, which exposes users to fragmented content governed by systems seen as unaccountable, further eroding trust. This aligns with Carrigan’s ( 2021 ) concept of epistemological chaos, where the collapse of consensus reality is accelerated by systems unfit for sovereign epistemic arbitration. The qualitative data on civic engagement shows the democratic cost: strategic withdrawal ( “Why comment? They will call you a zombie or a hater. Better to just watch.” ) and a retreat to private, homogeneous digital spaces like WhatsApp groups. This represents a tangible erosion of the public sphere (Habermas, 1989 ), a fragmentation into epistemic communities (DeVito et al., 2023 ) that undermines cross-cutting discourse. Moderated Effects and Resilient Structures in Canada In contrast, the Canadian context reveals a more moderated, though still significant, impact of platform governance. Higher baseline levels of institutional trust (Table 4 ) and digital literacy (Table 5 ) appear to provide a buffer. The significant but weaker path coefficients suggest that while technogarchy poses a clear threat, its effects are mediated by a more resilient public sphere and a policy environment actively seeking to counter digital harms (Government of Canada, 2024 ; Dubois & McKelvey, 2023 ). This supports Platform Society Theory (Van Dijck et al., 2018 ), which posits that platforms become embedded within distinct social and political orders. The thematic findings on epistemic vigilance exemplify this resilience. Canadian participants described verification strategies reliant on institutional infrastructure: “I reverse-image search and cross-reference with CBC or CTV... I check Snopes or Logically.” This contrasts sharply with the more social and relational verification ( “I call my brother who is a journalist” ) reported in Nigeria. Furthermore, while both groups exhibited algorithmic awareness, Canadians frequently framed it as a personal responsibility to manage— “I have to actively search for opposing perspectives” —whereas Nigerians described it more as an external, manipulative force. This reflects a higher sense of personal efficacy linked to greater digital literacy. However, the qualitative data also reveals cracks in this resilience. Themes of emotional and psychological toll— “digital exhaustion,” “cognitive load,” and cynical normalization ( “It’s the new normal. You assume a percentage of what you see is false” )—are prominent. This indicates that even within a more resilient structure, the sustained burden of navigating platform-induced disorder leads to fatigue and a dangerous lowering of truth expectations, threatening long-term democratic engagement. The universal moderating effect of digital literacy (H4), significant in both countries, is a crucial finding. It suggests that enhancing critical digital skills—source evaluation, algorithmic awareness, media bias recognition—can serve as a partial check on hermeneutic injustice, regardless of context. This aligns with Seppänen et al. ( 2024 ) on fighting misinformation and Anderson’s (2012) conception of epistemic justice as an institutional virtue. However, the lower mean literacy scores in Nigeria, reflected qualitatively in less frequent mention of tool-based verification, highlight a profound inequality in the distribution of this protective resource, exacerbating the epistemic vulnerability of its populace—a clear form of the systemic marginalization discussed by Sharma et al. ( 2024 ) and Zermeño-Flores et al. ( 2024 ). Beyond Fragmentation: The Crisis of Shared Hermeneutical Resources The analysis moves beyond merely noting information fragmentation to critiquing its epistemic consequences. The strong negative path from fragmentation to trust in journalism (H2) in both countries, but stronger in Nigeria, underscores a deeper crisis: the splintering of publics into filter bubbles (Van Dijck et al., 2018 ) destroys the shared interpretive frameworks necessary for democratic deliberation. Journalism’s role as a central narrator and validator of knowledge is fundamentally undermined. This is not just a filter bubble effect but a manifestation of hermeneutical injustice (Fricker, 2007 ; Dotson, 2014 ), where fragmented publics lack the collective resources to make sense of social experiences. The thematic analysis on civic engagement provides the behavioral correlate to this injustice. The widespread strategic, niche engagement—in private WhatsApp groups in Nigeria or well-moderated subreddits in Canada—is an adaptation to this splintering. While serving as a coping mechanism, it actively reinforces the epistemic enclaves that prevent the formation of shared understanding. Platform algorithms, optimized for engagement (Srnicek, 2019 ), actively engineer this splintering, creating polarized "counter-knowledge orders" (Schäfer, 2025 ) that further delegitimize journalistic authority. The resulting disillusioned withdrawal and information fatigue documented in both countries are direct symptoms of a public sphere struggling to function under these conditions. Technogarchy as a Symptom of Political and Institutional Failure The findings ultimately validate the critique that technogarchy is less a stable new order and more a symptom of a "failed triage." The profound accountability vacuum (Cobbe, 2021 ) and the privatization of epistemic authority (Törnberg, 2023 ) evident in the data point to antecedent political failures. States, whether in Nigeria due to capacity and legacy issues or in Canada due to regulatory hesitation, have ceded ground, forcing profit-driven platforms into the role of unelected arbiters of truth and speech. The thematic analysis on allocation of responsibility powerfully captures this failure from the citizen’s perspective. There is a strong, cross-national consensus on the platform accountability deficit, described through a lens of unfairness and opaque profit motive. However, the desired solutions diverge radically based on state legitimacy. In Canada, there is cautious hope in independent regulatory bodies, aligning with ongoing policy discourse (Government of Canada, 2024 ). In Nigeria, the state is viewed as part of the problem, leading to calls for non-state oversight. This divergence empirically underscores that the call for digital constitutionalism (De Gregorio, 2020 ) and models of cooperative responsibility (Helberger et al., 2018 ) must be context-specific. The study demonstrates that the solution cannot be a one-size-fits-all import of Global North governance models, which risk epistemic dispossession (Azeri, 2025 ) and perverse effects, such as silencing marginalized voices through culturally blind moderation policies, in the Global South. Conclusion: Toward Context-Sensitive Epistemic Repair In conclusion, this study provides compelling empirical evidence that technogarchy is a key driver of the contemporary epistemic crisis, but its power is variegated. It acts as a contextual amplifier, deepening historical injustices in settings like Nigeria while testing the resilient structures of democracies like Canada. The crisis is one of both knowledge and governance—a collapse in shared reality precipitated by opaque algorithmic systems that have inherited authority from faltering public institutions. Addressing this crisis requires moving beyond technical "solutions" like fact-checking alone. It demands context-sensitive epistemic repair that: 1) prioritizes radical transparency and public oversight of platform governance to address the accountability vacuum universally demanded by citizens in both contexts; 2) invests aggressively in universal digital literacy as a foundational civic skill, recognizing its moderating potential but also the inequitable global distribution of this resource; and 3) develops democratic governance models that re-embed platform infrastructures within locally legitimate public values and institutions, acknowledging the rational roots of distrust in post-colonial states while leveraging the cautious hope for accountable regulation in established democracies. The future of the digital public sphere (Habermas, 1989 ) depends not on perfecting technogarchy, but on democratically reclaiming epistemic authority from it, a task whose blueprint must be written with deep sensitivity to the historical and political contours of each platform society. Declarations Conflict of Interest: The Authors declare no conflict of interest. Ethical approval: The study received ethical approval from the Institutional Review Board of the University of Nigeria, Nsukka and University of Calgary, Canada. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent: Informed consent was obtained from all individual participants included in the study. Funding: There was no funding for this study Author Contribution Samuel sunday Ameh was responsible for ideation, research and data gathering. Nathan Oguche Emmanuel was responsible for data analysis and interpretation Data Availability The data for this study was gathered through survey and FGD in Nigeria and Canada. 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At the heart of this transformation lies the rise of \"technogarchy\"\u0026mdash;a mode of governance where unelected technical experts and algorithmic systems, embedded within digital platforms, exert significant control over public discourse and knowledge ecosystems (Larsson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kennedy, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This shift has precipitated a profound epistemic crisis, characterized by widespread public mistrust in institutions, rampant information fragmentation, and the erosion of shared factual frameworks (Sch\u0026auml;fer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Carrigan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study investigates the nexus between platform governance and this escalating epistemic crisis, arguing that the infrastructural power of platforms functions not merely as a conduit for information but as an active architect of public knowledge and trust. While the phenomenon is global, its manifestations are deeply contingent on local political, social, and media contexts. A comparative analysis of Nigeria, a post-colonial state with volatile public trust and complex information disorders, and Canada, an advanced democracy with distinct policy responses to digital harms, offers a critical lens to unpack this dynamic (Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Government of Canada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConceptually, this study interrogates several core variables. Technogarchy is operationalized as the concentration of epistemic authority in the hands of platform owners, engineers, and opaque algorithmic systems that curate content, moderate speech, and define community standards, often divorced from democratic accountability (T\u0026ouml;rnberg, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gillespie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Epistemic Crisis refers to the systemic breakdown in collective processes for distinguishing reliable knowledge from falsehood, leading to polarized counter-knowledge orders and a collapse in consensus reality (Sch\u0026auml;fer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Platform Governance encompasses the technical and policy mechanisms\u0026mdash;including algorithmic content moderation, fact-checking partnerships, and terms of service enforcement\u0026mdash;through which platforms regulate user behavior and information flow (Gorwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Katzenbach \u0026amp; Ulbricht, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Public Mistrust is defined as the erosion of confidence in traditional knowledge institutions (government, media, science) and their digital counterparts, often fueled by perceptions of bias, opacity, or failure (Roelofs, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Burns et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). And Information Fragmentation denotes the splintering of publics into isolated epistemic communities or \"filter bubbles,\" where algorithmic personalization and polarized content hinder exposure to diverse perspectives (Van Dijck et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, the symptoms of this crisis are stark. Studies indicate that false news online spreads significantly faster and farther than true information (Vosoughi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), a dynamic embedded within the broader architecture of information disorder (Wardle \u0026amp; Derakhshan, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is not a localized issue but a pervasive global challenge, evidenced by the documented surge of coordinated disinformation across continents (Africa Center for Strategic Studies, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the specific viral mechanisms that propel false political narratives in digital ecosystems (Oyewole \u0026amp; Adekunle, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tandoc et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Africa, disinformation has been weaponized to manipulate electoral processes and exacerbate social conflicts (Africa Center for Strategic Studies, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oyewole \u0026amp; Adekunle, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the Global North, concerns about foreign interference and domestic misinformation have prompted major policy initiatives (Dubois \u0026amp; McKelvey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Global Affairs Canada, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This environment is further complicated by epistemic injustices, where digital systems systematically marginalize certain voices and forms of knowledge based on identity, geography, or socio-economic status (Fricker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zerme\u0026ntilde;o-Flores et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe discourse surrounding platform governance and epistemic integrity is highly contested. Supporters of a technocratic approach argue that sophisticated AI-driven content moderation is essential for managing the scale of online speech and countering harmful disinformation (Gillespie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Griffin, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They posit that platforms, with their engineering prowess, are best positioned to develop agile solutions to information disorder. Antagonists, however, critique this model as a form of epistemic dispossession, where corporate-controlled algorithms enact opaque censorship, suppress legitimate dissent, and reinforce existing power structures without transparency or recourse (Azeri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cobbe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This camp advocates for robust public oversight, digital constitutionalism, and governance models that prioritize public values over profit and efficiency (De Gregorio, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Helberger et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eResearch Gap\u003c/h3\u003e\n\u003cp\u003eDespite this vibrant debate, a significant gap persists in comparative, context-sensitive analyses that link the macro-structures of platform capitalism (Srnicek, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zuboff, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to the micro-dynamics of public trust and epistemic health in divergent national settings. More so, a significant gap persists in comparative, context-sensitive analyses that link the macro-structures of platform capitalism to the micro-dynamics of public trust and epistemic health in divergent national settings. Much of the literature remains siloed, focusing either on Western democracies or on the Global South in isolation, with insufficient dialogue between the two (e.g., Burns et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Government of Canada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oyewole \u0026amp; Adekunle, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wasserman \u0026amp; Madrid-Morales, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, while the concepts of epistemic injustice and algorithmic governance are widely discussed, their concrete interplay in shaping public mistrust in specific national contexts\u0026mdash;particularly across the democratic spectrum from Canada to Nigeria\u0026mdash;remains under-theorized and inadequately empirically linked. The foregoing necessitates this study.\u003c/p\u003e \u003cp\u003eTherefore, this study is designed to address this gap. Its objectives are to: (1) comparatively analyze the manifestations of technogarchic governance on major social media platforms and their perceived role in fostering epistemic crisis in Nigeria and Canada. (2) examine the relationship between public exposure to platform-governed information environments and levels of trust in public institutions (government, media, health authorities) in both countries. (3) evaluate and contrast residents responses to platform-induced information disorders in Nigeria and Canada, assessing their implications for democratic resilience. By grounding this inquiry in a robust theoretical framework and a comprehensive empirical comparison, this study aims to contribute a nuanced understanding of how digital platform governance is intricately linked to the contemporary crisis of knowledge and trust, with vital implications for democracy, policy, and the future of the global digital public sphere.\u003c/p\u003e "},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eTechnogarchy and Algorithmic Gatekeeping\u003c/h2\u003e \u003cp\u003eThe framing of platform control as \"technogarchy\" represents a critical evolution from earlier theories of networked governance. While useful, the concept risks overstating the coherence and intentionality of what is often a fragmented, commercially-driven, and reactive system of content management. True technogarchy implies a unified ruling logic, yet platform governance is frequently characterized by internal contradictions\u0026mdash;between free speech imperatives and brand safety, between automated scale and contextual nuance, and between global policies and local societal norms (Gillespie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gorwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is not a seamless technocratic rulership but an ad-hoc algorithmic patchwork, where engineering solutions are continuously deployed to manage social and political crises they inadvertently help create.\u003c/p\u003e \u003cp\u003eFurthermore, the concept of algorithmic gatekeeping demands scrutiny of its supposed neutrality. Algorithms are not merely technical filters but embedded socio-political artifacts, encoding the values, biases, and commercial incentives of their creators (Noble, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The gatekeeping power thus shifts from journalistic editors, bound by professional ethics and public interest norms, to black-boxed systems optimized for engagement and growth (T\u0026ouml;rnberg, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This creates a profound accountability vacuum: decisions affecting public discourse are made by non-transparent systems, justified by appeals to \"community standards\" and \"scale,\" effectively shielding platforms from direct responsibility for epistemic harm (Cobbe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The critical issue, therefore, is not merely the concentration of power but its obfuscation and privatization, moving democratic oversight into the inaccessible realm of corporate proprietary code.\u003c/p\u003e \u003cp\u003eUltimately, technogarchy is less a description of a stable order and more a symptom of a failed triage. As states hesitate to impose robust digital constitutionalism (De Gregorio, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), platforms are forced into the role of sovereign arbiters, a function for which their profit-driven, engineering-centric culture is fundamentally ill-suited. The resulting system is neither competent nor legitimate, eroding trust not only in platforms but in the very idea of a shared informational reality. A critical analysis must therefore look beyond labeling this system as a new aristocracy and focus on the political and institutional failures that ceded such profound epistemic authority to private actors in the first place.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eDigital Journalism and Trust: Global North vs. Global South\u003c/h3\u003e\n\u003cp\u003eThe Global North/South dichotomy in analyzing media trust, while heuristically valuable, can perpetuate a neo-colonial analytical trap if it simplistically contrasts a \"mature\" versus \"deficient\" media landscape. The critical divide is not merely in the \u003cem\u003elevel\u003c/em\u003e of trust, but in its historical constitution and the role of the state. In many Global South contexts like Nigeria, low institutional trust is a legacy of post-colonial state formation, where media institutions were often tools of authoritarian control or elite patronage rather than public service (Akinola et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wasserman \u0026amp; Madrid-Morales, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Distrust is thus a rational, historically-grounded response to state performance, not just a symptom of digital disruption. In the Global North, by contrast, trust erosion often represents a fall from grace\u0026mdash;a breaking of a previously held social contract between established institutions and citizens, accelerated by platform-amplified scandals and elite polarization (Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis foundational difference shapes the very nature of digital threats. In the Global North, disinformation is often framed as an external contaminant (e.g., foreign interference) or a pathology of the periphery (e.g., fringe groups), threatening a presumably healthy center (Dubois \u0026amp; McKelvey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The policy response is thus regulatory and defensive, aiming to protect an existing system. In the Global South, disinformation is frequently an instrument of the core political establishment itself, deeply entwined with patronage networks and electoral politics (Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Africa Center for Strategic Studies, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Here, the state can be a primary actor in the trust crisis, not its potential savior, rendering simplistic \"fact-checking\" solutions inadequate. Consequently, the imposition of platform governance models and journalistic norms developed in the Global North can have perverse effects in the South. For instance, content moderation policies designed in Silicon Valley may silence marginalized activist voices or fail to recognize locally-specific forms of hate speech (Gorwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The critical analysis must therefore challenge the universalist assumptions embedded in both platform policies and much media development discourse. Rebuilding trust cannot follow a single blueprint; it requires bottom-up, context-specific strategies that address the unique historical, political, and social roots of distrust in each setting, acknowledging that for many in the Global South, distrust is not a problem to be solved but a rational stance to be engaged and understood.\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eTheoretical Framework: Platform Society Theory, Algorithmic Governance/Technocracy Theory and Epistemic Injustice Theory\u003c/b\u003e\u003c/div\u003e \u003cp\u003eThis study is theorized at the intersection of three critical frameworks that collectively explain the ascent of technogarchy and its role in fomenting epistemic crisis. At the macro-structural level, \u003cb\u003ePlatform Society Theory\u003c/b\u003e (Van Dijck, Poell, \u0026amp; de Waal, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) establishes the landscape: digital platforms have become the foundational infrastructure of public life, subordinating traditional institutions like journalism to their connective, data-driven logic. Within this society, \u003cb\u003ea technocratic form of algorithmic governance\u003c/b\u003e (T\u0026ouml;rnberg, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Larsson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Katzenbach \u0026amp; Ulbricht, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) constitutes the operational engine of power. This technogarchy\u0026mdash;rule by opaque systems and engineering elites\u0026mdash;governs through automated content moderation, personalized ranking, and surveillance (Gorwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gillespie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), actively constructing the informational environment. The societal outcome is not merely fragmentation but a pervasive \u003cb\u003eepistemic injustice\u003c/b\u003e (Fricker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dotson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), where these systems systematically enact testimonial injustice by de-legitimizing certain voices and hermeneutical injustice by depriving publics of shared interpretative resources (Azeri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sch\u0026auml;fer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis synthesized discourse directly animates our hypotheses. H1 (Platform governance \u0026rarr; Information fragmentation) is the direct outcome of algorithmic governance logics, which engineer filter bubbles and personalized realities for engagement (Srnicek, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). H2 (Fragmentation \u0026rarr; Distrust in journalism) is critically explained as a hermeneutic injustice; as shared frames of reference splinter, journalism\u0026rsquo;s role as a central narrator is undermined, and its claims are met with credibility deficits (Fricker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). H3 (Platform governance \u0026rarr; Distrust) reflects the direct, corrosive effect of technocratic authority, where platforms\u0026rsquo; opaque content moderation and algorithmic curation (Cobbe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) erode the perceived legitimacy of all intermediary institutions, creating a crisis of authority (Carrigan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The proposed moderators are tests of this theory\u0026rsquo;s contingent power. H4 posits that digital literacy can mitigate fragmentation, representing a potential check on hermeneutic injustice. Informed users (Sepp\u0026auml;nen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) may resist algorithmic enclosures, though this resistance is unevenly distributed, often along socio-economic lines (Noble, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most critically, H5 asserts that the country context moderates all paths. This is a direct test of the Platform Society\u0026rsquo;s core tenet of re-embedding, anticipating that the technogarchic logic will interact explosively with Nigeria\u0026rsquo;s pre-existing low institutional trust (Okunoye, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Akinola, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and politicized media (Wasserman \u0026amp; Madrid-Morales, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), compared to its interaction with Canada\u0026rsquo;s more resilient, though strained, public sphere (Burns et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Government of Canada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the study empirically investigates whether the epistemic injustice wrought by platform society is a universal outcome or a variegated one, disproportionately borne by contexts with legacies of informational and political inequality (Timcke \u0026amp; Wasserman, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStudy Hypotheses\u003c/h3\u003e\n\u003cp\u003eH1: Platform governance positively influences information fragmentation.\u003c/p\u003e \u003cp\u003eH2: Information fragmentation negatively influences trust in journalism.\u003c/p\u003e \u003cp\u003eH3: Platform governance negatively influences trust in journalism (direct effect).\u003c/p\u003e \u003cp\u003eH4: Digital literacy moderates the effect of platform governance on information fragmentation, such that higher literacy reduces fragmentation.\u003c/p\u003e \u003cp\u003eH5: Country (Nigeria vs Canada) moderates all structural paths, resulting in significant differences between contexts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cp\u003e \u003cstrong\u003eResearch Design\u003c/strong\u003e \u003cp\u003eMixed-methods research design was employed to comprehensively address the study\u0026rsquo;s objectives and test its hypotheses (Creswell \u0026amp; Plano Clark, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This two-phase approach was selected to first quantify the structural relationships between platform governance, information fragmentation, and trust (Objectives 1 \u0026amp; 2; H1-H5), and then to qualitatively explore and contrast the nuanced perceptions and behavioral responses of residents to platform-induced information disorders (Objective 3). Phase One utilized a quantitative, cross-sectional survey. Phase Two utilized semi-structured focus group discussions (FGDs) designed to elicit rich, comparative data on lived experiences, coping strategies, and perceived impacts on democratic engagement (se.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation, Sample Size, and Sampling Technique\u003c/h3\u003e\n\u003cp\u003eThe target population for the quantitative phase was defined as adult (18+) residents of Nigeria and Canada who are active users of at least one major social media platform (e.g., Facebook, X, Instagram, TikTok). According to recent national statistics and industry reports, this constituted an estimated 35\u0026nbsp;million adults in Nigeria and 30\u0026nbsp;million adults in Canada at the time of the study. The sample size for the survey in each country was calculated using the Krejcie and Morgan (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) formula for determining sample size from a finite population:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:s=\\frac{{X}^{2}NP(1-P)}{{d}^{2}(N-1)+{X}^{2}P(1-P)}\\)\u003c/span\u003e \u003c/span\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:s\\)\u003c/span\u003e \u003c/span\u003e = required sample size.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X}^{2}\\)\u003c/span\u003e \u003c/span\u003e = table value of chi-square for 1 degree of freedom at the desired confidence level (3.841 for 95%).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e \u003c/span\u003e = population size (35,000,000 for Nigeria; 30,000,000 for Canada).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e \u003c/span\u003e = population proportion (assumed to be 0.5 for maximum variability).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:d\\)\u003c/span\u003e \u003c/span\u003e = degree of accuracy (0.05).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eApplying this formula yielded a minimum sample size of approximately 385 per country. To account for potential non-response, ensure robustness for subgroup analysis, and meet the requirements of PLS-SEM, the target sample was increased. A final sample of 1,188 valid responses was obtained from Nigeria and 1,205 from Canada, providing high statistical power. A multi-stage sampling procedure was implemented in each country:\u003c/p\u003e\n\u003ch3\u003eIn Nigeria:\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eStage 1 (Geopolitical Stratification)\u003c/strong\u003e \u003cp\u003eThe country was divided into its six geopolitical zones (North-West, North-East, North-Central, South-West, South-East, South-South). A proportional quota of participants was allocated to each zone based on its adult population share.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 2 (State \u0026amp; Urban/Rural Selection)\u003c/strong\u003e \u003cp\u003eTwo states were randomly selected from each zone. Within each selected state, one major urban and one rural Local Government Area (LGA) were purposively selected to ensure diversity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 3 (Household \u0026amp; Respondent Selection)\u003c/strong\u003e \u003cp\u003eWithin each selected LGA, a random walk procedure was used to select households. In each household, the adult who most recently celebrated a birthday and was a social media user was selected to participate in the online survey, administered via tablet devices by trained field assistants.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIn Canada:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eStage 1 (Provincial Stratification)\u003c/strong\u003e \u003cp\u003eThe country was stratified by province and territory. Participants were allocated proportionally based on adult population figures from Statistics Canada.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 2 (Census Division Selection)\u003c/strong\u003e \u003cp\u003eWithin each province, three Census Divisions (CDs) were selected randomly.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStage 3 (Random Digit Dialing \u0026amp; Online Panel)\u003c/strong\u003e \u003cp\u003eA dual-frame approach was used. For landline and mobile frames, a list-assisted random digit dialing (RDD) technique was employed. This was supplemented by invitations to a pre-recruited, probability-based online panel (from a provider like the Angus Reid Forum) to ensure representation of younger demographics. Panel members received the survey link, and screening questions verified residency and social media use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFor the qualitative Phase Two, purposive sampling was used. From the survey respondents in each country, 80 participants (40 per country) who indicated willingness to be re-contacted were selected to form four Focus Group Discussions (FGDs) of 5 participants each in 8 different groups (per country). Selection ensured diversity in age, gender, digital literacy score (from the survey), and level of political concern regarding misinformation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInstrument for Data Collection\u003c/strong\u003e \u003cp\u003eTwo primary instruments were developed\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStructured Online Questionnaire\u003c/strong\u003e \u003cp\u003eThis instrument, as detailed previously, measured the core latent constructs (Platform Governance, Information Fragmentation, Trust in Journalism, Digital Literacy) using validated 7-point Likert scales (Gillespie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Dijck et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sepp\u0026auml;nen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFGD Guide\u003c/b\u003e: A semi-structured guide was developed for Phase Two. It contained open-ended questions and scenario-based prompts designed to probe residents' responses to information disorders. Key questions included: \"Describe a recent time you encountered information online you suspected was false or misleading. What specific actions, if any, did you take?\"; \"What do you believe is the responsibility of ordinary users versus platforms versus the government in addressing these problems?\"; and \"How do these experiences affect your willingness to participate in political discussions or community issues?\"\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMethod of Data Collection\u003c/strong\u003e \u003cp\u003eQuantitative data collection proceeded as described. For the qualitative phase, the FGDs were conducted virtually via secured video-conferencing software, recorded with consent, and lasted approximately 90 minutes each. Conducted twice every week (2 groups per week (5 individuals per group) and rounded up in two months for the two countries. They were facilitated by a trained moderator in the local language(s) (English and Pidgin for Nigeria; English and French for Canada), with a note-taker present.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReliability and Validity\u003c/strong\u003e \u003cp\u003eFor the survey, established psychometric protocols were followed (Hair et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pilot testing (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{Nigeria}=120\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{Canada}=120\\)\u003c/span\u003e\u003c/span\u003e) confirmed clarity and cultural appropriateness. In the main study, Composite Reliability (CR) scores for all constructs exceeded 0.85, and Average Variance Extracted (AVE) values were above 0.60, confirming reliability and convergent validity. For the FGDs, trustworthiness was ensured through triangulation (comparing findings with survey data), member checking (sharing summaries with participants for verification), and maintaining an audit trail of analytical decisions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMethod of Data Analysis\u003c/strong\u003e \u003cp\u003eAnalysis followed the two-phase design. Survey data were analyzed using PLS-SEM in SmartPLS 4.0 to test H1-H5. The FGD recordings were transcribed verbatim and analyzed using \u003cb\u003ethematic analysis\u003c/b\u003e (Braun \u0026amp; Clarke, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This involved familiarization with the data, generating initial codes, searching for themes (e.g., \"active verification,\" \"fatalistic disengagement,\" \"community-based correction\"), reviewing themes, and defining them. The themes were then compared and contrasted between the Nigerian and Canadian groups to address Objective 3 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Consideration\u003c/strong\u003e \u003cp\u003eThe study received ethical approval from the Institutional Review Board. All participants provided informed consent. Survey data were anonymized, and FGD participants were assigned pseudonyms. Data were stored on encrypted, password-protected servers. The research adhered to the Tri-Council Policy Statement (TCPS2) and the Nigerian National Code for Health Research Ethics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \n\u003ch3\u003eData Presentation and Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Survey Respondents This table presents the demographic profile of participants from both countries, ensuring representativeness across key variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNigeria (n\u0026thinsp;=\u0026thinsp;1,188)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada (n\u0026thinsp;=\u0026thinsp;1,205)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e358 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e634 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e582 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e542 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e603 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-binary/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e156 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome college/diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e412 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e378 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e356 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e445 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e226 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e784 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e912 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e293 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow \u003cspan\u003e$\u003c/span\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e487 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e500-\u003cspan\u003e$\u003c/span\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e389 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,500-\u003cspan\u003e$\u003c/span\u003e3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove \u003cspan\u003e$\u003c/span\u003e3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e596 (49.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e establishes the demographic representativeness of the sample across both countries, with Nigeria showing a younger population profile and lower income levels compared to Canada.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlatform Governance Perception Metrics by Country This table examines how respondents perceive the governance mechanisms of social media platforms, addressing Hypothesis 1 and Objective 1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Governance Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlgorithmic Content Curation Opacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.23 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.87 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContent Moderation Consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45 (1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.98 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Accountability Perception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.89 (1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.56 (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerms of Service Clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12 (1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUser Control Over Personal Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.67 (1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21 (1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Bias in Content Decisions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.45 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.65 (1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Platform Governance Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.80 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.97 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e*Note: All items measured on 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 7\u0026thinsp;=\u0026thinsp;Strongly Agree). **p\u0026lt;.001\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals significant differences in platform governance perceptions, with Nigerian respondents reporting higher opacity and bias perceptions while Canadians report better accountability and consistency.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation Fragmentation Indicators Across Countries This table assesses the extent of information fragmentation experienced by users, directly testing Hypothesis 1 regarding platform governance effects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Fragmentation Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to Diverse Political Views\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12 (1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89 (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilter Bubble Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.34 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.78 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcho Chamber Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.67 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.92 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-Cutting Content Encounter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.98 (1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.67 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Algorithmic Personalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.56 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.12 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Source Diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.23 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Fragmentation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.52 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.87 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e*Note: Higher scores indicate greater fragmentation. All items on 7-point scale. **p\u0026lt;.001\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates that information fragmentation is significantly more pronounced in Nigeria, supporting the contextual nature of platform effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrust In Journalism and Public Institutions By Country This table measures institutional trust levels, addressing Objective 2 and testing Hypotheses 2 and 3 regarding the relationship between fragmentation, platform governance, and trust.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Traditional Journalism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.67 (1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.89 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Digital News Platforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.23 (1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.12 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Government Communications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.89 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.98 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Health Authorities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.23 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-26.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Scientific Institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.12 (1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.45 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-21.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Social Media Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.45 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Fact-Checking Organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.78 (1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.67 (1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Institutional Trust Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.37 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.45 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-20.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e*Note: All items measured on 7-point scale (1\u0026thinsp;=\u0026thinsp;No Trust, 7\u0026thinsp;=\u0026thinsp;Complete Trust). **p\u0026lt;.001\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows substantially lower institutional trust across all dimensions in Nigeria compared to Canada, with particularly stark differences in trust in health authorities and government communications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital Literacy Levels and Their Moderating Effects This table presents digital literacy assessments and tests Hypothesis 4 regarding literacy as a moderator between platform governance and information fragmentation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource Evaluation Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.12 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFact-Checking Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.89 (1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.98 (1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlgorithmic Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.56 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-15.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia Bias Recognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.12 (1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.23 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Privacy Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.67 (1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.89 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Thinking Online\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.34 (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.34 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Mechanism Understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.78 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.78 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Digital Literacy Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.93 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.13 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-21.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e*Note: All components measured on 7-point scale (1\u0026thinsp;=\u0026thinsp;Very Low, 7\u0026thinsp;=\u0026thinsp;Very High). **p\u0026lt;.001\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates significantly higher digital literacy levels in Canada, which may serve as a protective factor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMulti-Group Pls-Sem Analysis Results Testing All Hypotheses This table presents the comprehensive structural equation modeling results, testing all five hypotheses and directly addressing Objective 3 regarding comparative responses and democratic resilience implications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized Path\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNigeria p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanada p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup Diff. Sig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1: Platform Gov \u0026rarr; Info Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.547***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.423***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2: Info Fragmentation \u0026rarr; Trust (Journalism)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.612***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.489***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3: Platform Gov \u0026rarr; Trust (Journalism)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.289***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Gov \u0026rarr; Trust (Government)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.345***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Gov \u0026rarr; Trust (Health Auth.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.298***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.187**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfo Fragmentation \u0026rarr; Trust (Government)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.412***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfo Fragmentation \u0026rarr; Trust (Health Auth.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.489***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.378***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4: Digital Literacy \u0026times; Platform Gov \u0026rarr; Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.167**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2; Info Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2; Trust in Journalism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2; Trust in Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Fit (SRMR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e*\u003cb\u003eNote\u003c/b\u003e\u003c/strong\u003e \u003cp\u003e \u003cb\u003e***p\u0026lt;.001, **p\u0026lt;.01, p\u0026lt;.05. β\u0026thinsp;=\u0026thinsp;standardized path coefficient. Δβ\u0026thinsp;=\u0026thinsp;difference in path coefficients between groups.\u003c/b\u003e \u003cem\u003eH5 is supported by significant group differences across multiple paths. Digital literacy moderating effect (H4) is significant in both countries but group difference is not significant, suggesting universal moderating effect.\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides strong support for all five hypotheses, with the multi-group analysis revealing that platform governance effects on fragmentation and trust are significantly stronger in Nigeria, confirming that country context moderates these relationships as predicted by H5. The moderating effect of digital literacy (H4) operates consistently across both contexts, suggesting this as a potential intervention point regardless of national context.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThematic Analysis Table: Comparative Responses to Platform-Induced Information Disorders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription of Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllustrative Data from Nigeria (Quotes/Participant Insights)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIllustrative Data from Canada (Quotes/Participant Insights)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComparative Analysis \u0026amp; Implications for Democratic Resilience\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1. Epistemic Vigilance \u0026amp; Verification Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive Skepticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants describe intentional efforts to question and verify information before accepting or sharing it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"I always check the source. If it's not a known newspaper or official handle, I don't trust it. But even then, I wait for others to comment.\" (Male, 28, Lagos)\u003c/p\u003e \u003cp\u003e\"I use WhatsApp forward checker sites and sometimes I call my brother who is a journalist.\" (Female, 42, Kano)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"I reverse-image search and cross-reference with CBC or CTV. If it's political, I check AP or Reuters.\" (Female, 34, Toronto)\u003c/p\u003e \u003cp\u003e\"I look at the domain, the author bio, and check Snopes or Logically.\" (Male, 51, Vancouver)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNigeria: Verification is often social and relational (relying on personal networks).\u003c/p\u003e \u003cp\u003eCanada: Verification is more institutional and tool-based (relying on legacy media and fact-checking sites).\u003c/p\u003e \u003cp\u003eImplication: Democratic resilience in Canada is underpinned by trust in institutionalized verification infrastructure; in Nigeria, resilience is more fragile and dependent on interpersonal trust.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlgorithmic Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecognition that platforms curate and personalize content, influencing what is seen.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Facebook only shows you what you already like. It keeps you in your tribe.\" (Male, 35, Port Harcourt)\u003c/p\u003e \u003cp\u003e\"They push trends that cause arguments, not truth.\" (Female, 29, Abuja)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"The algorithm feeds me content that confirms my views. I have to actively search for opposing perspectives.\" (Non-binary, 23, Montreal)\u003c/p\u003e \u003cp\u003e\"I know my feed is a bubble, but breaking it feels like work.\" (Female, 45, Calgary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBoth groups demonstrate algorithmic awareness, but Canadians more frequently describe it as a personal responsibility to \"break the bubble,\" while Nigerians often describe it as a deliberate platform strategy to inflame conflict. This aligns with Tapper \u0026amp; Bronstein's (2025) concept of \"engineering epistemic churn.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2. Civic Engagement \u0026amp; Democratic Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisillusioned Withdrawal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncountering misinformation leads to disengagement from online political discourse.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Why comment? They will call you a zombie or a hater. Better to just watch.\" (Male, 40, Enugu)\u003c/p\u003e \u003cp\u003e\"After the election lies, I left Twitter. It's not for truth.\" (Female, 38, Ibadan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"I avoid commenting on political posts. The replies are toxic and fact-free.\" (Male, 60, Ottawa)\u003c/p\u003e \u003cp\u003e\"I've muted keywords and unfollowed polarizing pages. I've essentially opted out.\" (Female, 31, Halifax)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eShared response: Self-protective disengagement. This represents a direct threat to democratic resilience by shrinking the participatory public sphere (Habermas, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The withdrawal is more pronounced in Nigeria, linked to experiences of politicized trolling and post-election trauma (Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategic, Niche Engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipation continues but in safer, more homogenous digital spaces.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"We have a WhatsApp group for our community where we share and debate real news.\" (Female, 50, Sokoto)\u003c/p\u003e \u003cp\u003e\"I only discuss politics in my close friends' Instagram group.\" (Male, 26, Lagos)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"I engage in well-moderated subreddits or dedicated Discord servers where rules are enforced.\" (Male, 29, Edmonton)\u003c/p\u003e \u003cp\u003e\"I'll share vetted information from trusted NGOs, but not on my main feed.\" (Female, 47, Toronto)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA retreat to epistemic communities (DeVito et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Suri et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While preserving some discourse, this reinforces fragmentation and undermines the cross-cutting discourse essential for a healthy democracy. This is a lived manifestation of information fragmentation leading to hermeneutical injustice (Dotson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3. Allocation of Responsibility \u0026amp; Desired Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform Accountability Deficit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong perception that platforms are primarily responsible for the problem but are failing to act fairly or transparently.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"They ban the poor man's post but leave the big politician's lies. Who is regulating them?\" (Male, 33, Kaduna)\u003c/p\u003e \u003cp\u003e\"Their fact-checking partners are in Lagos and don't understand our context here.\" (Female, 44, Maiduguri)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"They profit from outrage. Why would they stop it? Their community standards are applied unevenly.\" (Female, 52, Winnipeg)\u003c/p\u003e \u003cp\u003e\"Transparency reports are just PR. We need to see how the algorithms actually work.\" (Male, 38, Vancouver)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCritical Consensus: Both groups exhibit profound mistrust in platform governance, viewing it as opaque and biased. Nigerians emphasize contextual ignorance and political bias (Gorwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while Canadians focus on profit motives and lack of transparency. This fuels the broader public mistrust documented in the survey.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState Role: Distrust vs. Cautious Hope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiverging views on the appropriate role of government intervention.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Government cannot be the referee. They are the main players spreading the lies!\" (Female, 36, Abuja)\u003c/p\u003e \u003cp\u003e\"Maybe an independent body, but not government-controlled.\" (Male, 41, Port Harcourt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"We need something like the Digital Safety Commission they're proposing, but it must be independent and arm's-length from politics.\" (Female, 55, Ottawa)\u003c/p\u003e \u003cp\u003e\"The Online News Act was a start, but it's messy.\" (Male, 48, Toronto)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey Divergence: In Nigeria, the state is widely viewed as a primary malicious actor in the information disorder, making state-led solutions illegitimate. In Canada, there is cautious support for technocratic policy initiatives (Government of Canada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) though skepticism remains. This directly tests H5 and confirms that rebuilding trust cannot follow a single blueprint.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4. Emotional \u0026amp; Psychological Toll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInformation Fatigue \u0026amp; Cynicism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe constant need to navigate misinformation induces exhaustion and a generalized cynicism towards all information.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"You are tired. Every news now looks like lie. You just switch off.\" (Male, 55, Benin City)\u003c/p\u003e \u003cp\u003e\"It has killed my interest in politics. Everything is propaganda.\" (Female, 31, Jos)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"I feel digitally exhausted. The cognitive load of verifying everything is unsustainable.\" (Female, 39, Montreal)\u003c/p\u003e \u003cp\u003e\"It's led to a kind of nihilism. How can we agree on anything if we can't agree on facts?\" (Male, 44, Calgary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis theme is crucial. The psychological cost of epistemic crisis\u0026mdash;information fatigue\u0026mdash;erodes the citizen energy required for democratic engagement. It is a barrier to resilience not captured by trust metrics alone. Authors like Tusikov (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Krawchenko \u0026amp; Rahmani (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) allude to this societal cost in the Canadian context, while it is a pervasive lived reality in Nigeria.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResigned Normalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA sense that misinformation is an inevitable, normalized part of the digital landscape.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Fake news is now normal. It's part of social media, like adverts.\" (Male, 22, Lagos)\u003c/p\u003e \u003cp\u003e\"We have learned to live with it. You just pray you are not the one deceived.\" (Female, 57, Ilorin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\"It's the new normal. You assume a percentage of what you see is false.\" (Female, 62, Regina)\u003c/p\u003e \u003cp\u003e\"We've become desensitized. The shock value is gone.\" (Male, 36, Quebec City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis normalization represents a profound democratic danger: the collective lowering of expectations for truth in public discourse. It signifies an adaptation to epistemic crisis (Sch\u0026auml;fer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) rather than resistance to it, undermining the very precondition for deliberative democracy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eThis table presents thematic analysis findings from eight Focus Group Discussions (FGDs) conducted across Nigeria and Canada (four FGDs per country, n\u0026thinsp;=\u0026thinsp;40 participants total 80 in the two countries). Themes were identified through iterative thematic analysis (\u003c/em\u003eBraun \u0026amp; Clarke, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003cem\u003e) and triangulated with survey findings. Quotes have been lightly edited for clarity while preserving participant voice and meaning.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e thematic analysis reveals that while platform governance is a shared stressor, its interaction with local context produces divergent resilience profiles. Nigeria exhibits fragile, network-dependent resilience characterized by high withdrawal and deep suspicion of all authority (state and platform). Canada demonstrates institutional-reliant resilience, with citizens leveraging trusted entities but facing fatigue and cynical normalization. Both paths point toward democratic erosion\u0026mdash;through either fragmentation and withdrawal or cynical disengagement.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion of Findings","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003cp\u003eThe empirical findings of this comparative study reveal a complex and context-dependent landscape where platform governance conceptualized as technogarchy does not operate as a uniform, monolithic force. Instead, it functions as a powerful amplifier, intensifying pre-existing societal fractures related to institutional trust, historical legacies, and civic resilience. The data robustly supports the core thesis that the epistemic crisis of public mistrust and information fragmentation is intrinsically linked to platform governance, but the magnitude and nature of this linkage are decisively moderated by national context, as hypothesized (H5). The qualitative thematic analysis provides rich, contextual depth to these quantitative relationships, illustrating precisely \u003cem\u003ehow\u003c/em\u003e these dynamics manifest in the lived experiences and strategic behaviors of citizens in both countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe Amplification of Historical Distrust in Nigeria\u003c/h2\u003e \u003cp\u003eThe significantly stronger path coefficients in Nigeria (e.g., Platform Gov \u0026rarr; Info Frag: β\u0026thinsp;=\u0026thinsp;0.547 vs. 0.423 in Canada) demonstrate that technogarchic governance interacts explosively with a context of pre-existing low institutional trust. This finding critically engages with the literature on post-colonial media landscapes (Wasserman \u0026amp; Madrid-Morales, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Akinola et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Nigeria, distrust is not merely a product of digital disruption but a \"rational, historically-grounded response\" to state performance and media histories intertwined with authoritarian control. Platform governance, characterized by high perceived opacity and bias (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), does not create distrust \u003cem\u003ede novo\u003c/em\u003e but rather systemically compounds it. The algorithmic curation and opaque content moderation (Gillespie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gorwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provide a new, technologically sophisticated layer to existing patterns of epistemic injustice (Fricker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), where voices are already marginalized and hermeneutical resources are scarce.\u003c/p\u003e \u003cp\u003eThe thematic analysis vividly illustrates this compounding effect. Participants\u0026rsquo; allocation of responsibility reveals a profound conviction that the state is a primary malicious actor in the information disorder, with statements like, \u003cem\u003e\u0026ldquo;Government cannot be the referee. They are the main players spreading the lies!\u0026rdquo;\u003c/em\u003e This directly corroborates the literature framing disinformation as an instrument of the core political establishment (Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Africa Center for Strategic Studies, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, platform governance becomes a contested terrain in these ongoing struggles, perceived as politically biased\u0026mdash;\u003cem\u003e\u0026ldquo;They ban the poor man\u0026rsquo;s post but leave the big politician\u0026rsquo;s lies.\u0026rdquo;\u003c/em\u003e This perception fuels the vicious cycle identified quantitatively: low trust in institutions drives reliance on social media, which exposes users to fragmented content governed by systems seen as unaccountable, further eroding trust. This aligns with Carrigan\u0026rsquo;s (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) concept of epistemological chaos, where the collapse of consensus reality is accelerated by systems unfit for sovereign epistemic arbitration. The qualitative data on civic engagement shows the democratic cost: strategic withdrawal (\u003cem\u003e\u0026ldquo;Why comment? They will call you a zombie or a hater. Better to just watch.\u0026rdquo;\u003c/em\u003e) and a retreat to private, homogeneous digital spaces like WhatsApp groups. This represents a tangible erosion of the public sphere (Habermas, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), a fragmentation into epistemic communities (DeVito et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that undermines cross-cutting discourse.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eModerated Effects and Resilient Structures in Canada\u003c/h2\u003e \u003cp\u003eIn contrast, the Canadian context reveals a more moderated, though still significant, impact of platform governance. Higher baseline levels of institutional trust (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and digital literacy (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) appear to provide a buffer. The significant but weaker path coefficients suggest that while technogarchy poses a clear threat, its effects are mediated by a more resilient public sphere and a policy environment actively seeking to counter digital harms (Government of Canada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dubois \u0026amp; McKelvey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This supports Platform Society Theory (Van Dijck et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which posits that platforms become embedded within distinct social and political orders.\u003c/p\u003e \u003cp\u003eThe thematic findings on epistemic vigilance exemplify this resilience. Canadian participants described verification strategies reliant on institutional infrastructure: \u003cem\u003e\u0026ldquo;I reverse-image search and cross-reference with CBC or CTV... I check Snopes or Logically.\u0026rdquo;\u003c/em\u003e This contrasts sharply with the more social and relational verification (\u003cem\u003e\u0026ldquo;I call my brother who is a journalist\u0026rdquo;\u003c/em\u003e) reported in Nigeria. Furthermore, while both groups exhibited algorithmic awareness, Canadians frequently framed it as a personal responsibility to manage\u0026mdash;\u003cem\u003e\u0026ldquo;I have to actively search for opposing perspectives\u0026rdquo;\u003c/em\u003e\u0026mdash;whereas Nigerians described it more as an external, manipulative force. This reflects a higher sense of personal efficacy linked to greater digital literacy. However, the qualitative data also reveals cracks in this resilience. Themes of emotional and psychological toll\u0026mdash;\u003cem\u003e\u0026ldquo;digital exhaustion,\u0026rdquo; \u0026ldquo;cognitive load,\u0026rdquo;\u003c/em\u003e and cynical normalization (\u003cem\u003e\u0026ldquo;It\u0026rsquo;s the new normal. You assume a percentage of what you see is false\u0026rdquo;\u003c/em\u003e)\u0026mdash;are prominent. This indicates that even within a more resilient structure, the sustained burden of navigating platform-induced disorder leads to fatigue and a dangerous lowering of truth expectations, threatening long-term democratic engagement.\u003c/p\u003e \u003cp\u003eThe universal moderating effect of digital literacy (H4), significant in both countries, is a crucial finding. It suggests that enhancing critical digital skills\u0026mdash;source evaluation, algorithmic awareness, media bias recognition\u0026mdash;can serve as a partial check on hermeneutic injustice, regardless of context. This aligns with Sepp\u0026auml;nen et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on fighting misinformation and Anderson\u0026rsquo;s (2012) conception of epistemic justice as an institutional virtue. However, the lower mean literacy scores in Nigeria, reflected qualitatively in less frequent mention of tool-based verification, highlight a profound inequality in the distribution of this protective resource, exacerbating the epistemic vulnerability of its populace\u0026mdash;a clear form of the systemic marginalization discussed by Sharma et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Zerme\u0026ntilde;o-Flores et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBeyond Fragmentation: The Crisis of Shared Hermeneutical Resources\u003c/h2\u003e \u003cp\u003eThe analysis moves beyond merely noting information fragmentation to critiquing its epistemic consequences. The strong negative path from fragmentation to trust in journalism (H2) in both countries, but stronger in Nigeria, underscores a deeper crisis: the splintering of publics into filter bubbles (Van Dijck et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) destroys the shared interpretive frameworks necessary for democratic deliberation. Journalism\u0026rsquo;s role as a central narrator and validator of knowledge is fundamentally undermined. This is not just a filter bubble effect but a manifestation of hermeneutical injustice (Fricker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dotson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), where fragmented publics lack the collective resources to make sense of social experiences.\u003c/p\u003e \u003cp\u003eThe thematic analysis on civic engagement provides the behavioral correlate to this injustice. The widespread strategic, niche engagement\u0026mdash;in private WhatsApp groups in Nigeria or well-moderated subreddits in Canada\u0026mdash;is an adaptation to this splintering. While serving as a coping mechanism, it actively reinforces the epistemic enclaves that prevent the formation of shared understanding. Platform algorithms, optimized for engagement (Srnicek, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), actively engineer this splintering, creating polarized \"counter-knowledge orders\" (Sch\u0026auml;fer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) that further delegitimize journalistic authority. The resulting disillusioned withdrawal and information fatigue documented in both countries are direct symptoms of a public sphere struggling to function under these conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTechnogarchy as a Symptom of Political and Institutional Failure\u003c/h2\u003e \u003cp\u003eThe findings ultimately validate the critique that technogarchy is less a stable new order and more a symptom of a \"failed triage.\" The profound accountability vacuum (Cobbe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the privatization of epistemic authority (T\u0026ouml;rnberg, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) evident in the data point to antecedent political failures. States, whether in Nigeria due to capacity and legacy issues or in Canada due to regulatory hesitation, have ceded ground, forcing profit-driven platforms into the role of unelected arbiters of truth and speech. The thematic analysis on allocation of responsibility powerfully captures this failure from the citizen\u0026rsquo;s perspective. There is a strong, cross-national consensus on the platform accountability deficit, described through a lens of unfairness and opaque profit motive. However, the desired solutions diverge radically based on state legitimacy. In Canada, there is cautious hope in independent regulatory bodies, aligning with ongoing policy discourse (Government of Canada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Nigeria, the state is viewed as part of the problem, leading to calls for non-state oversight. This divergence empirically underscores that the call for digital constitutionalism (De Gregorio, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and models of cooperative responsibility (Helberger et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) must be context-specific. The study demonstrates that the solution cannot be a one-size-fits-all import of Global North governance models, which risk epistemic dispossession (Azeri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and perverse effects, such as silencing marginalized voices through culturally blind moderation policies, in the Global South.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion: Toward Context-Sensitive Epistemic Repair","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cp\u003eIn conclusion, this study provides compelling empirical evidence that technogarchy is a key driver of the contemporary epistemic crisis, but its power is variegated. It acts as a contextual amplifier, deepening historical injustices in settings like Nigeria while testing the resilient structures of democracies like Canada. The crisis is one of both knowledge and governance\u0026mdash;a collapse in shared reality precipitated by opaque algorithmic systems that have inherited authority from faltering public institutions. Addressing this crisis requires moving beyond technical \"solutions\" like fact-checking alone. It demands context-sensitive epistemic repair that: 1) prioritizes radical transparency and public oversight of platform governance to address the accountability vacuum universally demanded by citizens in both contexts; 2) invests aggressively in universal digital literacy as a foundational civic skill, recognizing its moderating potential but also the inequitable global distribution of this resource; and 3) develops democratic governance models that re-embed platform infrastructures within locally legitimate public values and institutions, acknowledging the rational roots of distrust in post-colonial states while leveraging the cautious hope for accountable regulation in established democracies. The future of the digital public sphere (Habermas, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) depends not on perfecting technogarchy, but on democratically reclaiming epistemic authority from it, a task whose blueprint must be written with deep sensitivity to the historical and political contours of each platform society.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eThe Authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval:\u003c/strong\u003e \u003cp\u003e The study received ethical approval from the Institutional Review Board of the University of Nigeria, Nsukka and University of Calgary, Canada. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent:\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThere was no funding for this study\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSamuel sunday Ameh was responsible for ideation, research and data gathering. Nathan Oguche Emmanuel was responsible for data analysis and interpretation\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data for this study was gathered through survey and FGD in Nigeria and Canada. 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PublicAffairs\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Technogarchy, Epistemic Crisis, Comparative Analysis, Platform Governance, Digital Literacy","lastPublishedDoi":"10.21203/rs.3.rs-8816392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8816392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis comparative study investigates how platform governance conceptualized as \"technogarchy\"\u0026mdash;amplifies epistemic crisis, characterized by public mistrust and information fragmentation. Through a mixed-methods design (survey: n\u0026thinsp;=\u0026thinsp;2,393; focus groups: n\u0026thinsp;=\u0026thinsp;80), we analyze the divergent contexts of Nigeria and Canada. Structural equation modelling reveals that platform governance significantly increases fragmentation and erodes institutional trust, with effects markedly stronger in Nigeria (β\u0026thinsp;=\u0026thinsp;0.547 vs. 0.423). Digital literacy moderates this relationship in both countries. Qualitative findings expose contextual resilience profiles: Nigerians exhibit network-dependent, fragile resilience amid deep state distrust, while Canadians show institutional-reliant resilience burdened by digital fatigue. The study concludes that technogarchy acts not as a uniform force but as a contextual amplifier, exacerbating pre-existing historical and political fractures. It empirically links algorithmic governance to epistemic injustice, demonstrating that the crisis of shared reality is disproportionately borne by post-colonial contexts. The findings demand context-sensitive epistemic repair, prioritizing transparent platform oversight, equitable digital literacy, and democratic governance models that re-embed platforms within locally legitimate public values.\u003c/p\u003e","manuscriptTitle":"Technogarchy and Epistemic Crisis: Linking Platform Governance to Public Mistrust and Information Fragmentation — A Comparative Study of Nigeria and Canada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 08:37:38","doi":"10.21203/rs.3.rs-8816392/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4046be8b-2824-491c-997a-f390901bb7b9","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T08:37:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 08:37:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8816392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8816392","identity":"rs-8816392","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}