Fact-Checking Organisations in China: Characteristics, Verification Approaches, and Practices

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Yet existing research remains largely Western-centric, overlooking practices in authoritarian and hybrid media systems. Addressing this gap, this study examines the organisational characteristics, issue selection logics, and verification approaches of fact-checking entities in mainland China. Drawing on a mixed-methods design, it analysed 2,719 fact-checking articles from seven institutional actors and 3,026 video texts from three individual fact-checkers on Chinese TikTok (Douyin) between 2022 and 2025. The study integrated BERTopic modelling and manual content analysis, with 400 sampled cases assessed across verification dimensions such as claim accessibility, verdict style, evidentiary support, and citation diversity. Based on metadata, a fourfold typology was constructed: official, professional/commercial, independent/academic, and individual models. Findings show that Chinese fact-checking operates within a pluralistic yet state-integrated ecosystem shaped by political sensitivities, platform affordances, and audience expectations. Verification functions as a culturally embedded performance of epistemic authority. By conceptualising fact-checking as institutional boundary work, the study contributes to comparative debates on misinformation governance beyond liberal-democratic contexts. Humanities/Cultural and media studies Social science/Cultural and media studies Social science/Politics and international relations fact-checking cultures misinformation governance verification practices authoritarian media systems BERTopic Figures Figure 1 Figure 2 Figure 3 Introduction Fact-checking is the systematic, evidence-based evaluation of claims made by public actors, including politicians, journalists, and social media users (Graves & Amazeen, 2019; Vinhas & Bastos, 2022). It originated in Western liberal-democratic societies, where specialised organisations such as FactCheck.org (est. 2003) institutionalised the practice by issuing gradated verdicts like “true” or “misleading” (Graves, 2016; Cortada & Aspray, 2020; Amazeen, 2020). The 2010s marked a period of rapid global expansion, driven by concern over “fake news” and the proliferation of “alternative facts,” which undermined shared epistemic norms (Bennett & Livingston, 2020; Gordon, 2018; Cooke, 2018). In this context, fact-checking was redefined as both a safeguard for journalistic integrity and a corrective against democratic erosion. The institutionalisation of the field culminated in the establishment of transnational regulatory frameworks like the International Fact-Checking Network 1 (IFCN, 2015) and the European Fact-Checking Standards Network 2 (EFCSN, 2022), which codify principles such as neutrality, transparency, and ethical rigor (Beaudreau, 2024; Frau-Meigs & Corbu, 2024). By January 2025, the World Trends database of the United Nations Educational, Social and Cultural Organization (UNESCO) documented the existence of hundreds of nonpartisan fact-checking organisations around the world 3 . The IFCN has certified 155 signatory organisations across more than 60 countries, highlighting the field's institutional maturation and transnational reach 4 . In addition to the aforementioned measures, the Duke University Reporters' Lab recorded 451 active fact-checking initiatives across 117 countries 5 . Platform collaborations and funding schemes, such as the Global Fact Check Fund 6 , have further augmented institutional capacity. Empirical studies demonstrate that fact-checking reduces public misperceptions, improves knowledge retention, and deters political actors from repeating falsehoods (Nyhan et al., 2020; Carey et al., 2022; Larraz et al., 2024). However, scholarly engagement remains heavily Western-centric. Most literature focuses on organisations such as PolitiFact, FactCheck.org, and Full Fact, analysing editorial routines and influence on public discourse (Graves, 2016; Walter et al., 2020; Graves & Cherubini, 2016). Country-level studies have centred on the US, UK, Spain, Germany, and Portugal (Cortada & Aspray, 2020; Cushion et al., 2022; Calvo et al., 2022), while comparative work explores variations in media trust and misinformation exposure across Western democracies (Mahl et al., 2024; Joux & Teixeira, 2024; López-Marcos & Vicente-Fernández, 2021; Humprecht, 2020). These studies often adopt Western media typologies—liberal, democratic-corporatist, and polarised pluralist (Hallin & Mancini, 2004; Brandtzaeg et al., 2018; Cazzamatta, 2025a)—which risk marginalising non-Western fact-checking as imitative or deficient. Scholarship on Asia, Africa, and Latin America remains limited (Lelo, 2024; Liu & Zhou, 2022; Cheruiyot & Ferrer-Conill, 2018), and few studies examine verification practices in hybrid or authoritarian systems (Cazzamatta, 2025b). Mainland China presents a salient yet under-theorised case in this regard. As of 2025, the Duke Reporters' Lab lists only six Chinese fact-checking initiatives—five in Hong Kong and one in the mainland, none IFCN-certified. This absence reflects not inactivity, but rather China's unique governance structures, where fact-checking is embedded within broader rumour-management systems operated by state-affiliated or commercial actors. Such hybrid configurations complicate comparisons with liberal-democratic models and demand new theoretical frameworks sensitive to authoritarian media logics and constrained civic space. The Fact-Checking Movement in China The fact-checking ecosystem of mainland China is characterised by institutional diversity, encompassing state media outlets, commercial platforms, academic research laboratories, and individual contributors. These initiatives have been developed in response to evolving policy imperatives, platform architectures, and information legitimacy crises. As early as 2011, People's Daily initiated the "Qiu Zhen" (求真) column with the objective of combating online rumours 7 . In 2015, China's criminal law was amended with a view to criminalising the deliberate spread of false information 8 . In that same year, Tencent launched "Jiao Zhen" (较真) 9 , which remains the only mainland-based fact-checking initiative listed by the Duke University Reporters' Lab (2025). In the academic sphere, Nanjing University initiated the "He Zhen Lu" (核真录) classroom-based verification project in 2017 10 . In 2018, the Cyberspace Administration of China and Xinhua News Agency established the Chinese Internet United Rumour-Debunking Platform (中国互联网联合辟谣平台) 11 . Subsequent to this, independent and professional initiatives were implemented: "China Fact Check" (有据, 2020) 12 focuses on international misinformation, while ThePaper launched "FactPaper" (澎湃明查) in 2021 13 . Despite their diversity, most initiatives operate within state-linked communication infrastructures, functioning more as public educators than adversarial watchdogs. Empirical scholarship remains limited. For instance, Fang (2021) examined the operations of China Fact Check using in-depth interviews. Liu and Zhou (2022), using a comparative content analysis, identified divergences between Jiao Zhen and the U.S.-based PolitiFact, particularly in claim types and transparency of sources. Zhang (2025), utilising interviews with FactPaper journalists, found that although methods and tools are disclosed, editorial decision-making is opaque and subject to institutional oversight. Others explore media effects: Chen and Neo (2024), using a 3×3 between-subjects survey design, found that levels of government trust remained high even when misinformation was corrected via verified sources. Similarly, Wang et al. (2022) found that Chinese misinformation tends to concern lifestyle rather than politics. Methodologically, the field remains fragmented. While Zhang et al. (2024) and Hu et al. (2022) began constructing Chinese-language fact-checking corpora, comparative, large-scale datasets remain scarce. Outside of China, the English-language commentary has emphasised China’s “rumour governance” paradigm and state-centric information control. Global media outlets and think tanks often frame Chinese fact-checking practices as extensions of narrative management. For example, Shin and Thorson (2017) argued that the professed commitment to truth is frequently undercut by instrumental political goals. Lam (2018) illustrated how authorities have historically labelled most unofficial information as “rumours.” While foreign observers may recognise certain localised innovations in China’s fact-checking practices, they frequently frame the country as an outlier instead of considering it a context where new institutional forms are emerging from within (Montaña-Niño et al., 2024). Recent scholarship notes that Chinese platforms have adapted Western verification models under political supervision (Quelle et al., 2025; Hokkanen, 2025), but most studies remain case-bound and lack typological synthesis. For instance, Wei (2024) and Zhang (2025) highlighted the influence of state ideology on editorial routines, these observations have rarely been embedded within a unified analytical framework. Meanwhile, a new cohort of individual fact-checkers on platforms like Douyin has emerged, relying on visual storytelling and participatory demonstrations (Lu & Shen, 2023), yet their practices are largely absent from academic accounts. This study addresses these gaps by developing a typological framework that examines Chinese fact-checking as a set of institutionalised communicative practices. Rather than treating China as a deviation from liberal-democratic standards, we conceptualise it as a distinct epistemic regime embedded in specific institutional and political logics. A Proposed Typology of Fact-Checking Models and Verification Cultures This section develops an analytical framework for comparing Chinese fact-checking practices across three dimensions: organisational configuration, issue selection, and verification methodology. We first outline the primary institutional models found within China’s fact-checking landscape; next, we examine how each model dictates the selection of topics and accomplishes the verification of information. The section concludes with the formulation of the study’s three research questions. 1). Organisational Configuration Understanding fact-checking as a culturally situated practice necessitates attention to its organisational embeddedness. Drawing on Knorr Cetina’s (2007) concept of “epistemic cultures” and Hanitzsch’s (2007) notion of “journalism cultures,” we conceptualise the organisational frame not as a static background but as a constitutive force that shapes how fact-checking is envisioned, legitimised, and operationalised (Luengo & García-Marín, 2020). In the Chinese context, attributes such as an entity’s founding history, institutional affiliation, staffing structure, funding logic, and declared mission are not trivial metadata—they are integral to understanding the institutional logic of fact-checking. Rather than classifying organisations based solely on their legal status or funding source, this propesed framework treats these attributes as dynamic variables that influence normative orientations and editorial boundaries. For instance, organisational affiliation mediates editorial autonomy and public trust (Ceron et al., 2019); funding sources affect resource availability and perceived independence (Gras & Mendoza-Abarca, 2014); and staff backgrounds shape the epistemological assumptions underpinning verification standards (Godler & Reich, 2013). This perspective aligns with Graves’ (2018) assertion that fact-checkers often operate within hybrid institutional environments, straddling the arenas of journalism, academia, and platform governance. By analytically foregrounding these organisational dimensions, we can better trace how institutional mandates materialise into fact-checking routines and how these routines reflect the broader norms of information governance (Koliska & Roberts, 2025). Indeed, in authoritarian or hybrid regimes such as China’s, these organisational configurations intersect with state logics of information control, requiring researchers to account for visible infrastructures as well as latent ideological alignments (Landerer, 2013). 2). Issue Selection The question of what is considered worth checking is far from neutral (Graves, 2016). Issue selection is a strategic and context-sensitive process that reflects and reproduces the institutional cultures and knowledge regimes in which fact-checkers operate. Building on Hardy (2021) and Grubert (2020), we conceptualise issue selection as the interface between organisational mandates and the evolving misinformation environment. In China’s case, this environment is shaped not only by audience demand and platform virality but also by regulatory red lines, political sensitivities, and algorithmic filtering. Our study treats issue selection as a cultural practice—one that encodes values about public interest, legitimacy, and informational risk. The proposed typology enables a comparative analysis of how different models weigh these values. As Stocking and Gross (1989) have noted, the act of choosing what to verify inevitably implicates editorial bias, resource feasibility, and audience expectations. In China, such choices are further complicated by the need to avoid topics deemed politically sensitive or unverifiable under prevailing state information regimes (Liu & Zhou, 2022). Thus, analysing issue selection reveals how fact-checking organisations negotiate regulatory compliance, public trust, and editorial pragmatism. It also uncovers the adaptive strategies employed by different models, whether through topical specialisation, audience responsiveness, or reliance on user-submitted claims and platform analytics. 3). Verification Methodology Verification lies at the core of fact-checking cultures. As Graves (2017) argued, verification is both a procedural and epistemological act: fact-checkers not only collect evidence but also structure truth claims in ways that resonate with their institutional logic and their audience’s expectations. In China, this process is shaped by state authority, technological platforms, and domain-specific norms. This study adopts a verification-centred approach to explore how truth is constructed across institutional contexts. Key dimensions—such as claim accessibility, verdict style, visual evidence, and citation diversity—serve as diagnostic indicators of what we term “verification cultures” (Kim et al., 2022; Wei, 2023). In Western contexts, fact-checkers often prioritise transparency through hyperlinking, source documentation, and multi-level verdicts (Typografia et al., 2024). However, as Nenno (2024) and Venturini (2019) have suggested, binary verdicts (true/false determinations) often fail to capture the complexity of real-world claims. This is particularly relevant in China, where preferred epistemic strategies vary across models. For instance, official fact-checkers may favour categorical outcomes to project institutional certainty, while academic initiatives might adopt narrative or interpretive styles that accommodate uncertainty or controversy. Studying these strategies allows us to evaluate how verification serves different epistemic and communicative functions—from maintaining political legitimacy to fostering civic education or facilitating user engagement. As Moreno Gil et al. (2022) noted, the legitimacy of verification depends not only on procedural rigour but also on contextual sensitivity—a condition especially salient in politically mediated environments. 4). Research Questions Informed by the preceding theoretical elaborations, this study is guided by three interrelated research questions: RQ1: What organisational features characterise the fact-checking landscape in mainland China, and how do these features shape institutional norms and operational mandates? RQ2: How do fact-checking organisations in China differ in their issue selection strategies, and what do these differences reveal about the interplay between institutional priorities and audience dynamics? RQ3: How do information verification practices vary across different institutional models in China, and what epistemic assumptions underpin these variations? Methods 1. Country selection Mainland China provides a theoretically generative yet under-theorised case in global fact-checking scholarship. Although absent from the IFCN signatory list, its verification landscape offers critical insight into how misinformation governance unfolds outside liberal-democratic settings. China's fact-checking ecosystem is embedded in a distinctive nexus of state media governance, platform regulation, and constrained civic space, demanding new frameworks beyond Western-derived models such as the liberal or democratic-corporatist paradigms (Mahl et al., 2024). Fact-checking in China functions less as an independent watchdog and more as part of a broader “rumour governance” infrastructure. Initiatives span state media, commercial portals, university-based labs, and individual creators (e.g., Daddy Lab). This institutional heterogeneity reveals divergent verification logics, yet also underscores asymmetries in editorial autonomy and epistemic authority. State-affiliated models benefit from access to official sources but avoid politically sensitive topics, while individuals rely on platform visibility and focus on depoliticised content like health and consumer claims. These asymmetries highlight how institutional embedding shapes both what is verified and how verification is performed. The act of fact-checking reflects broader negotiations over truth, legitimacy, and communicative authority. Responding to recent calls to contextualise verification within sociotechnical environments (Zhang, 2025; Xiang & Neo, 2024), this study treats China not as a normative deviation but as a distinct epistemic regime. By tracing how different models operate under asymmetric regulatory conditions, we develop a typology that contributes to a more pluralistic theory of global verification cultures. 2. Sample and Data Collection To examine verification cultures across diverse organisational models in mainland China, we employed a purposive sampling strategy based on systematic keyword searches across national media platforms, social media databases, and prior empirical literature (Zhang et al., 2024). Entities were included if they had published at least 50 fact-checking outputs, either as written articles or annotated short videos. Based on these criteria, we selected ten fact-checking entities (comprising seven institutional actors and three prominent individual fact-checkers) representing a broad spectrum of organisational models (see Table 1). The selection process was not limited to specific platforms, such as China National Knowledge Infrastructure or Baidu, but incorporated triangulated data from media coverage, academic reports, and direct observations of platform activities. This broader scope ensured the inclusion of short video debunking, which is underrepresented in existing studies (Zhou, 2021; Zeng, 2021). While not exhaustive, the sample captured major institutional types, thematic domains, and verification styles, offering a valid basis for model-specific comparisons. Table 1 Study Sample Entity types Name Fact-checking articles/video texts ( n = 3,428) Fact-checking articles ( n = 2,719) Individual fact-checker video texts All video texts ( n = 3,026) Fact-checking video texts ( n = 709) Institutions Qiu Zhen 86 2.52% Wen Zheng 442 12.89% Chinese Internet United Rumour-Debunking Platform 912 26.60% Jiao Zhen 212 6.18% FactPaper 494 14.41% China Fact Check 533 15.54% He Zhen Lu 40 1.17% Individuals Fei Die Shuo 792 83 2.43% XLab 437 101 2.95% Daddy Lab 1797 525 15.31% To compare the verification cultures, we constructed two datasets. First, we compiled organisational metadata from verified platform profiles and official websites, including the sites’ affiliation type, staffing composition, funding model, and declared mission. These data were supplemented with academic sources to contextualise the institutional strategies and constraints. This dataset informed RQ1, which examined the organisational features of each model. Second, we constructed a corpus of fact-checking content, including 2,719 text-based articles produced by seven institutions between January 2022 and January 2025, obtained by using Python-based scraping tools. In parallel, we extracted 3,026 short video transcripts from the verified accounts of three individual fact-checkers (XLab, Daddy Lab, Fei Die Shuo) via Douyin 14 . Only content that explicitly debunked a specific claim was included, in line with standard definitions of fact-checking (Zhang et al., 2021). To ensure validity, a two-stage filtering process was employed, comprising (1) automated keyword matching (e.g., false, misleading, verified) and (2) manual annotation by two trained coders (Lu & Shen, 2023), who assessed whether each entry clearly addressed and corrected misinformation. A total of 709 short videos were retained, yielding an inter-coder reliability score of 89%. These datasets jointly support the analyses in RQ2 (issue selection) and RQ3 (verification practices). 3. Measures and Analyses This study employed a mixed-methods approach, integrating computational topic modelling, qualitative institutional analysis, and manual content coding to ensure methodological robustness and alignment with the three research questions. To address RQ1, we conducted a qualitative analysis of each entity’s institutional profile, comprising the following elements: founding year; affiliation type (e.g., official, commercial, academic, individual); declared mission; funding model (e.g., public budget, advertising, donation-based); and staffing background (e.g., journalism, academia, non-professionals). These features were extracted from the platform profiles and then cross-validated with academic and media sources (Liu & Zhou, 2022; Zhang et al., 2024; Yang et al., 2024). Institutional actors such as Qiu Zhen, Jiao Zhen, and Wen Zheng were compared with academic (He Zhen Lu) and individual (Daddy Lab) models to highlight divergences in editorial autonomy, scope, and legitimacy strategies. To address RQ2, we used BERTopic (v0.16.4) to conduct topic modelling of 2,719 articles and 709 video transcripts. We first applied Uniform Manifold Approximation and Projection (v0.5.7) for dimensionality reduction, followed by Hierarchical Density-Based Spatial Clustering of Applications with Noise (v0.8.4) for clustering. After removing 2.7% of noise points, we identified 237 topic clusters, which were manually reviewed and consolidated into 127 coherent topics and then grouped into 14 thematic clusters (e.g., domestic politics, education, technology, COVID-19, society). This process revealed model-specific editorial preferences and blind spots. To address RQ3, we performed a manual content analysis on a stratified sample of 400 items (40 items per entity) that were randomly drawn from the full corpus. A structured coding scheme was developed based on prior literature (Vu et al., 2023; Mahl et al., 2024). The coding variables comprised the following six elements: (1) claim accessibility (0 = not provided, 1 = directly provided, 2 = indirectly provided); (2) verdict style (0 = none, 1 = categorical, 2 = narrative); (3) claimant type (e.g., social media user, government official); (4) use of visuals (0 = none, 1 = present); (5) citation of sources (e.g., official reports); and (6) source diversity (0 = none, 1 = one source type, 2 = two or more source types). Inter-coder reliability was tested on a 10% subsample (n = 40), yielding Krippendorff’s α ≥ 0.89 for all variables. This manual layer enabled the close analysis of the verification forms, degrees of transparency, and underlying epistemic grammars. Together, the triangulated analyses produced a granular map of verification cultures, clarifying how each model constructs credibility, authority, and trustworthiness. Empirical Findings: Model-Specific Patterns in Issue Selection and Verification Practices This section presents the empirical findings in alignment with the three research questions, using a typological comparison across the ten sampled fact-checking entities. For additional institutional details, see Supplementary Material Tables B and C. 1. Fact-Checkers' Organisational Configurations Addressing RQ1, the organisational landscape of Chinese fact-checking entities can be categorised into four institutional models: the official model (public-oriented), the professional/commercial model (market-oriented), the independent/academic model (non-profit-oriented), and the individual fact-checking model (autonomy-oriented). Each model demonstrates unique characteristics in terms of its affiliation, staffing, funding mechanisms, and mission statements (see Table 2). The official model includes Qiu Zhen, Wen Zheng, and the Chinese Internet United Rumour-Debunking Platform. These entities are affiliated with central government agencies or state-owned media and serve authoritative roles in rumour regulation and information dissemination. They are typically staffed by trained journalists with backgrounds in political communication, and they are funded through institutional or public budgets, which shield them from commercial pressures. Thematically, they focus on domestic politics, COVID-19, and public security, aligning with state priorities in crisis communication and policy clarification. The professional/commercial model comprises Jiao Zhen and FactPaper, which are part of major digital media conglomerates. These organisations follow market-oriented logics, monetising through advertising and platform partnerships. Their editorial teams often combine journalism, platform strategy, and data analytics. They prioritise high-traffic topics such as scams, health misinformation, international affairs, and lifestyle content, with themes such as COVID-19, the US, and international politics dominating their coverage. The independent/academic model comprises China Fact Check and He Zhen Lu. These are non-commercial entities that are either fully independent or university-affiliated. He Zhen Lu operates as a classroom-based academic initiative, while China Fact Check functions as a charitable project targeting international misinformation in Chinese. Both enjoy relatively high editorial autonomy and tend to focus on international news, social controversies, and critical reviews of Western media narratives. The individual fact-checking model comprises Daddy Lab, Fei Die Shuo, and XLab—short video-based creators that are active primarily on Douyin. Their content is visual, platform-optimised, and highly interactive. These creators focus on product testing, science education, and health claims, often using participatory demonstrations. While not professionally trained in journalism, the creators frequently possess domain-specific expertise (e.g., chemistry, engineering). Their funding depends on platform incentives and commercial sponsorship, which can create tensions between popularity and epistemic rigour. Despite their differences, all four models exhibit shared commitments to accuracy and procedural consistency, although a formal affiliation with global standards (e.g., the IFCN) remains rare due to China's distinct governance structures. Table 2 Organisational Configurations of Fact-Checking Models in China Name Model Lauch time Affiliation Staff backgrounds Funding mechanisms Mission Qiu Zhen Official platform Official model 2011 People's Daily Professional journalists Public financial allocations and institutional budgets Maintain information authenticity, combat online rumours, and safeguard public trust in government and media. Wen Zheng Official platform 2020 Xinhua News Agency Professional journalists Chinese Internet United Rumour-Debunking Platform Official platform 2018 Cyberspace Administration of China Professional journalists Release authoritative information to dispel rumours, enhance netizens' media literacy, and create a clear and healthy online environment. Jiao Zhen Commercial platform Professional/commercial model 2015 Tencent News Professional journalists Diversified funding model Check the real and fake news in daily life. FactPaper Professional platform 2021 ThePaper Professional journalists Build a global fact-checking platform that is more comprehensive, professional, open, and interactive, serving as a “calibrator” for global hot topics and public events. China Fact Check Independent groups Independent/academic model 2020 Nonprofit independent start-up Multidisciplinary team Purely charitable project with no external investment Fact-check international news in Chinese. He Zhen Lu Academic groups 2017 Nanjing University's School of Journalism and Communication Professors and students from the School of Journalism and Communication at Nanjing University Reliance on colleges and academic partnerships for funding Evaluate the accuracy of factual statements in Chinese media news reports. Fei Die Shuo Individual fact-checkers Individual fact-checking model 2018 Independent start-up Non-professional Diversified funding model Product testing and health claims verification. XLab Individual fact-checkers 2019 Independent start-up Non-professional Debunk science and health misinformation. Daddy Lab Individual fact-checkers 2018 Independent start-up Non-professional Debunk tech myths and misinformation. 2). Fact-Checkers' Issue Selection Addressing RQ2, the topic modelling and qualitative comparison revealed distinct editorial priorities shaped by institutional logics (see Figure 1 and Supplementary Table B). 2.1 Thematic Priorities Across Organisational Models Fact-checking topics in China span the political, social, economic, health, and international domains, with notable divergences across the four institutional models. Each model reflects specific institutional mandates and audience orientations, resulting in divergent topical emphases. The official model places considerable emphasis on domestic politics (30.07%), public security and military (13.06%), and COVID-19 (12.99%), underscoring its core mandate to preserve political legitimacy and social stability. It also addresses society (8.06%), justice and crime (7.22%), and food and health (5.97%), consistent with state-led priorities in crisis communication and public administration. Topics related to Xinjiang and Tibet are visibly present, reflecting this model’s role in safeguarding the narratives concerning national unity. In contrast, there is minimal engagement with technology (0.69%), the US (1.53%), and Hong Kong/Macau (0.14%), indicating a cautious stance toward internationally sensitive themes. The professional/commercial model prioritises high-visibility, viral topics, including international politics (35.13%), COVID-19 (13.03%), and the US (11.47%). Additional focus areas include domestic politics (10.91%) and Hong Kong/Macau (7.93%), highlighting this model’s responsiveness to geopolitical controversies and audience curiosity. Cross-strait issues frequently appear, reflecting this model’s market-driven emphasis on high-engagement, politically charged narratives. The independent/academic model shares some similarities with the professional/commercial model—especially regarding international politics (33.86%) and the US (16.06%), but key distinctions emerge upon closer analysis. These actors prioritise Asia-related topics and focus their U.S. coverage on public figures such as Donald Trump and Elon Musk. The model is characterised by its attention to transnational misinformation, critical monitoring of Western media narratives, and value-laden controversies. These include women’s rights (6.38%), cross-strait relations (4.32%), and military activity (6.57%). Additionally, this model engages with the topics of justice and crime (4.01%), public transportation (4.54%), and environment and climate change (3.84%), typically through a critical epistemological lens. The individual fact-checking model demonstrates a grassroots orientation, with topical emphases on economy (27.08%), food and health (23.70%), education (16.50%), and society (14.81%). This reflects a focus on everyday concerns, including product safety, dietary practices, holiday customs, and family life, illustrating this model’s commitment to audience engagement and practical relevance. Across all of the models, some topics demonstrate cross-cutting significance. For instance, justice and crime, public transportation, and environment and climate change are relatively well-covered by the official model (7.22%, 4.58%, 3.89%), the professional/commercial model (3.68%, 3.12%, 2.97%), and the independent/academic model (4.01%, 4.54%, 3.84%), indicating a shared institutional interest in infrastructural and regulatory concerns. Conversely, cross-strait relations appear predominantly in the professional and academic models, while Hong Kong/Macau-related misinformation is addressed almost exclusively by the professional/commercial model (7.93%). These thematic distributions highlight not only divergent editorial logics but also the structural constraints and ideological positions that shape issue selection across China’s fact-checking ecosystem. 2.2 Sub-Thematic Variations and Cross-Model Dynamics Significant intra-cluster differences further distinguish the fact-checking models within shared thematic categories (see Supplementary Table B). Within the broad category of “society”—covered by the official (8.06%), professional/commercial (0.71%), independent/academic (10.82%), and individual (14.81%) models—each demonstrates distinct sub-thematic orientations, shaped by the institutional identity and communicative strategy. The official model primarily targets misinformation related to social unrest, patriotic mobilisation, major public events, sensational domestic incidents, and elderly care policies. This aligns with the state’s interest in reinforcing stability narratives and clarifying contentious mass incidents or ideological interpretations. The professional/commercial model exhibits a preference for highly relatable and socially resonant issues, such as public opinion manipulation, matchmaking and dating culture, overtime work disputes, and labour rights. These topics are frequently selected based on their virality and capacity to engage younger, urban online audiences, reflecting the model’s responsiveness to trending social discourse. The independent/academic model engages deeply with value-laden controversies, focusing on media manipulation, gender equity, rights of marginalised groups, and ethically contentious issues such as surrogacy. This approach reflects a more critical and epistemologically driven orientation. The individual fact-checking model emphasises everyday relevance, focusing on misinformation related to product safety, reproductive and women’s rights, traditional Chinese behavioural norms, festive customs, and family-centred social practices such as the Spring Festival travel season. These creators employ accessible narratives and culturally resonant formats to debunk social myths and clarify distorted popular beliefs. These sub-thematic distinctions not only reflect divergent editorial strategies but also reveal the varying degrees of institutional risk tolerance and discursive ambition across the models. Together, these patterns delineate a differentiated yet interdependent ecosystem of issue selection in the contemporary Chinese fact-checking field. 2.3 Issue Selection Practices and Criteria Complementing the quantitative analysis, insights from news reports and policy documents shed light on how the different models operationalise issue selection. Professional/commercial platforms (e.g., FactPaper) do not adhere to formal topic frameworks. Instead, they prioritise real-time responsiveness to trending misinformation, especially regarding global hotspots, viral rumours, and geopolitical controversies. Notably, FactPaper emphasises tracking foreign-origin falsehoods, particularly those shaping global perceptions of China (FactPaper, 2023). The official model follows explicit editorial mandates prioritising political stability, national unity, and crisis control, often selecting topics that align with state-defined ideological boundaries and communication goals (People's Daily, 2023). Independent/academic fact-checkers pursue diversification strategies to maintain public trust and avoid perceived bias. China Fact Check, for instance, engages with international rumours within Sinophone contexts, balancing analytical rigour with reputational neutrality. These actors often frame their selection as both a professional duty and a knowledge-building endeavour, emphasising transparency in methods and source credibility. The individual fact-checking model demonstrates a distinct focus on practical misinformation affecting daily life, such as consumer safety, health tips, and culturally specific claims, including common misinterpretations of festivals or traditional customs (Lu & Shen, 2023). This model’s prioritisation is driven largely by audience feedback, topical resonance, and platform-specific engagement metrics. All of the models utilise multi-channel discovery tools, including keyword alerts, platform analytics, user submissions, and proprietary monitoring systems. However, coverage of offline misinformation—such as political rumours or grassroots protests—remains limited, indicating a digital-trace dependency and low-risk orientation. Despite their operational differences, the fact-checkers across all four of the models apply shared selection criteria: assessing a claim’s social harm, potential for virality, and verifiability. Claims that are opinion-based, satirical, or unverifiable (e.g., conspiracy theories) are routinely excluded. This methodological discipline aligns with international standards, where falsifiability is essential for claim eligibility (He Zhen Lu, 2024). Ultimately, issue selection is shaped by each model’s institutional identity, epistemic stance, and audience engagement logic. Official actors act as the guardians of political order; professional/commercial platforms function as trend-sensitive communicators; independent/academic groups operate as critical analysts; and individual fact-checkers work as community-based myth debunkers. Although thematic overlaps exist, the practices are filtered through distinct mandates and constraints, producing a multi-scalar, pluralistic fact-checking environment. 3). Fact-Checkers' Information Verification In response to RQ3, the manual content analysis revealed systematic differences in the verification performance across the four models (see Table 3; Figures 2 and 3; Supplementary Table C; Figures A and B). Table 3 Comparison of Accessibility, Type of Verdict, Visual Elements Used, and Number of Resource Types Official ( n = 1440 articles) Professional/Commercial ( n = 706 articles) Independent/Academic ( n = 573 articles) Individual Fact-Checking ( n = 709 video texts) Qiu Zhen Wen Zheng Chinese Internet United Rumour-Debunking Platform Jiao Zhen FactPaper China Fact Check He Zhen Lu Fei Die Shuo XLab Daddy Lab Accessibility not provide 13 (10.9%) 2 (2.5%) — 48 (40%) direct provide 63 (52.5%) 47 (58.75%) 51 (63.75%) 32 (26.67%) indirect provide 44 (36.6%) 31 (38.75%) 29 (36.25%) 40 (33.33%) Verdict no verdict — — — — definitive verdict 120 (100%) 70 (87.5%) 66 (82.5%) 85 (70.83%) narrative verdict — 2 (2.5%) 16 (20%) 35 (29.17%) Visuals 90 (75%) 66 (82.5%) 79 (98.75%) 120 (100%) Resource no source — — — — one type 84 (70%) 24 (30%) 10 (12.5%) 85 (70.83%) two or more types 36 (30%) 56 (70%) 70 (87.5%) 35 (29.17%) 3.1 Accessibility of Claims Claim accessibility varies significantly across the models, and it is shaped by both platform affordances and institutional constraints. The official (10.9%) and individual (40%) models show the highest proportion of fact-checks lacking direct access to original claims. Official fact-checkers frequently summarise their claims using concise, declarative formulations, relying on institutional authority and public trust to legitimise their assessments without linking to source content. In contrast, individual fact-checkers often paraphrase claims, cite anonymous sources, or omit direct references, due to platform restrictions (e.g., algorithmic moderation, link suppression) and the structural limitations of short-form video formats. By comparison, the professional/commercial (58.75%) and independent/academic (63.75%) models demonstrate a strong preference for direct accessibility—including screenshots, hyperlinks, and timestamped references—thereby reinforcing procedural transparency. 3.2 Verdict Types: Categorical Clarity vs. Narrative Nuance Categorical verdicts dominate China’s fact-checking landscape. The official model applies definitive labels in 100% of the cases, while the professional/commercial (87.5%) and independent/academic (82.5%) models also favour this approach. Common verdicts include "false," "misleading," or "true," often presented with icons or colour-coded labels to enhance user comprehension. The official model uses these unambiguous verdicts to assert its institutional legitimacy and ensure clarity in politically sensitive settings. Professional/commercial platforms also benefit from categorical verdicts, particularly for enhancing readability on mobile interfaces. In contrast, the individual model uses categorical verdicts in only 70.83% of cases. A substantial 29.17% of its assessments take a narrative form, using process-based explanations or demonstrations. This reflects an educational orientation focused on the audiences’ understanding of the material. Similarly, independent/academic actors issue narrative verdicts in 20% of cases, particularly when dealing with scientific uncertainty or regulatory ambiguity. 3.3 Claimants and Targeted Scrutiny As illustrated in Figure 2, social media users are the most frequently identified claimants across all models: official (80.8%), professional/commercial (77.5%), and independent/academic (66.25%). This trend highlights a collective concern with viral misinformation originates from grassroots platforms, rather than from political elites or institutions. For the official model, this focus supports its mission of managing public discourse and mitigating bottom-up disruptions. Professional/commercial platforms, although similarly focused, present a broader claimant profile, influenced by audience attention and platform metrics. By contrast, the individual model shows the most diversified profile: In that group, 56.67% of claims are attributed to social media users, while 12.5% originate from the science and research sector, 10.83% from healthcare institutions, and 7.5% from the civil sector. This reflects the model’s grassroots positioning and domain-specific responsiveness. The professional/commercial and academic models are also more likely to challenge institutional or transnational sources, including traditional media (3.75%), new media (8.75% and 15%), and international political actors (6.25% and 3.75%). This reflects a relatively higher degree of editorial independence and a willingness to contest elite or foreign narratives, a flexibility not afforded to official actors, who avoid such scrutiny for political reasons. 3.4 Visual and Evidentiary Support All of the models rely on visual material, but the form and purpose vary. Individual fact-checkers use visuals in 100% of cases, including comparative product tests, infographics, and animated explainers optimised for short videos. Independent/academic models (98.75%) frequently use screenshots from videos, news sites, or official sources, particularly in addressing media manipulation and transnational misinformation. Professional/commercial platforms (82.5%) favour infographic storytelling and data visualisation, particularly when addressing public health or technology topics. In contrast, the official model (75%) primarily employs government-issued documents, policy papers, or legal records to affirm institutional authority. These visuals serve a declarative function rather than offering an interpretive analysis. 3.5 Source Citation and Epistemic Hierarchy As shown in Figure 3, citation practices reflect each model’s epistemic orientation and knowledge hierarchy. The official model relies predominantly on government departments (54%), public security agencies (47%), and traditional media (33%), illustrating a top-down model that is grounded in state epistemology. Professional/commercial platforms combine official (18%) and traditional (33%) media sources, with an increasing utilisation of international governmental data (13%) and medical institutions (40%), constructing hybrid evidentiary chains suited to a mass audience with broad topical interests. Independent/academic models exhibit the highest reliance on academic literature and in-house resources (57%), supplemented by diverse external references—such as international governments (27%) and traditional media (48%)—underscoring their epistemic autonomy and methodological stringency. In contrast, individual fact-checkers prioritise empirical and scientific sources (39%), followed by health authorities (21%), civil knowledge (18%), and social media (18%), emphasising hands-on validation and experiential expertise. These stratifications are further evidenced in source diversity: 70% of outputs by the official and individual models cite only one type of source, while the professional/commercial (70%) and academic (87.5%) models routinely draw from multiple types. These disparities shape not only the robustness of the verification but also its perceived legitimacy. 3.6 Practices and Challenges in Verification All models employ tools such as reverse image searches, keyword monitoring, and geolocation to trace misinformation provenance. However, engagement with original claimants remains rare due to potential reputational or legal risks. Large-scale platforms (e.g., Tencent, ThePaper) provide limited institutional support (such as legal or psychological counselling), and independent creators often lack formal protective mechanisms, underscoring the systemic vulnerabilities within China's verification ecosystem. On epistemological grounds, official and commercial entities prefer categorical verdicts, facilitating communicative clarity under time constraints. Academic models delay or refrain from issuing judgements in uncertain cases, preserving rigour over reach. Individual fact-checkers compensate for lower institutional legitimacy by crowdsourcing evidence, inviting user input, and offering transparent testing processes, thereby fostering participatory verification cultures. In conclusion, the verification practices of China’s fact-checking models are not merely technical operations but expressions of institutional logic, resource environments, and public trust frameworks. Official actors anchor legitimacy in the state, commercial platforms in visibility and speed, academics in epistemic rigour, and individuals in credibility-through-practice. These dynamics illustrate that verification in China is both a methodological discipline and a culturally embedded communicative act, negotiating contested terrains of authority, evidence, and truth. Discussion 1). Fact-Checking Cultures: Areas of Divergence and Convergence At the national level, China’s fact-checking field demonstrates a form of institutional pluralism operating within a unifying logic of political containment and epistemic control. Rather than existing in outright conflict, the four fact-checking models coalesce around a broader architecture shaped by China's state-led information order. Official actors such as the Chinese Internet United Rumour-Debunking Platform exemplify state-integrated epistemic infrastructures in which verification is a communicative function of governance. However, even independent and academic initiatives, while discursively distinct, often navigate implicit boundaries that discourage confrontation with politically sensitive claims. These institutionalised boundaries do not merely restrict content; they normalise selective silence, cultivating a field where public trust is managed rather than negotiated. This system-wide orientation suggests that China's fact-checking field serves as a regulatory mechanism of controlled transparency, upholding civic informational without compromising state sovereignty. At the model level, divergences in institutional affiliation, editorial practice, and verification logic manifest in distinct modes of truth performance. The official model operationalises verification as a bureaucratic mandate: Verdicts are categorical, claim sources are often unspecified, and citations overwhelmingly favour state-sanctioned data. This reaffirms institutional authority while limiting epistemic diversity. In contrast, commercial platforms adopt audience-centric responsiveness, employing real-time monitoring tools and diversified sources to maintain virality and credibility. Their output often blends professional routines with algorithmic optimisation, producing highly visual, mobile-optimised content that favours engagement over investigative depth. Independent/academic initiatives are unique in that they emphasise procedural integrity and international orientation. Their frequent use of narrative verdicts, multi-source triangulation, and critical discourse positions them as epistemic boundary agents, expanding verification from declarative truth assessment to reflective knowledge critique. Meanwhile, individual fact-checkers, although structurally marginal, inject experiential authority into the verification landscape. Their pragmatic focus on consumer safety, product testing, and health misinformation leverages platform logics to cultivate trust through embodied evidence and public accessibility. Despite these differences, convergent patterns are observable. Most of the models demonstrate growing adherence to internationally recognised fact-checking norms: transparency in verdicts, source citation, and multimodal evidence presentation. These convergences reflect not only methodological maturation but also a subtle process of institutional mimicry, where newer actors emulate the routines, aesthetics, and normative postures of their more established peers. This mimetic isomorphism, driven by shared environmental pressures (e.g., platform regulation, user expectations, credibility benchmarks), is reshaping the field into a functionally differentiated yet stylistically aligned verification ecosystem. However, this convergence has epistemic costs. The avoidance of sensitive domains—such as corruption, religion, or ethnic issues—indicates a tacit editorial consensus across the models. This is less a matter of legal censorship than of strategic self-limitation and reputational risk management. Academic and independent groups, although rhetorically committed to pluralism, often mirror official risk-avoidance strategies to maintain their access, visibility, and survival. Thus, what appears as diversity in operational form masks deeper conformity in issue avoidance—a form of epistemic convergence shaped by regulatory habituation rather than direct coercion. Conclusion This study has demonstrated that fact-checking in mainland China should be understood not merely as a response to misinformation but also as a socially embedded, institutionally mediated mode of epistemic governance. Drawing from a tripartite framework—focusing on organisational logics, issue selection, and verification practices—we reconceptualise fact-checking as a cultural form of boundary work. Rather than functioning as a neutral or adversarial truth regime, fact-checking in China operates through a complex interplay of authority, constraint, and adaptation. Across four institutional models (official, professional/commercial, independent/academic, and individual), verification practices reflect both functional differentiation and normative alignment. These patterns emerge not from policy coercion alone but through institutional emulation, reputational calibration, and negotiated legitimacy within a state-integrated media environment. Conceptually, this research contributes to the theorisation of fact-checking cultures by revealing how verification practices mediate between institutional mandates and public expectations. We have demonstrated that verification is not just a technical process of claim adjudication—it is a performative act through which actors define epistemic legitimacy, allocate evidentiary weight, and navigate normative boundaries. The models differ in operational scope as well as in how they frame truth—through categorical judgement, narrative reasoning, or visual demonstration—each encoding distinct assumptions about authority, audience, and credibility. By foregrounding these epistemic grammars, this study advances a situated understanding of how truth claims are constructed, authorised, and circulated in hybrid politically systems. Methodologically, the study illustrates the analytical power of mixed-methods research in capturing both structural patterns and contextual nuance. By integrating computational topic modelling with stratified content analysis, we have mapped thematic diversity while tracing micro-level verification logics. The result is a typological portrait that respects institutional specificity without reducing complexity to binaries of state-versus-market or control-versus-resistance. The sampling strategy, grounded in operational output rather than institutional status, also opens new possibilities for comparative studies across fragmented, platform-governed media ecosystems. Looking ahead, the framework developed here invites further research on how verification cultures emerge, adapt, or dissolve under shifting technological and political conditions. Cross-national comparisons—particularly among Global South societies or in authoritarian-capitalist contexts—may uncover alternative models of epistemic labour, including grassroots, adversarial, and transnational forms. Declarations Ethical considerations Not applicable. Author contribution statement Author 1: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing, Project administration. Consent to participate Not applicable. Consent for publication Not applicable. Declaration of conflicting interest The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding statement The author(s) disclosed receipt of financial support for the research, authorship, and/or publication of this article from a regional research center (details redacted for peer review). Data availability The data used in this study are drawn from publicly available news and video sources. Notes 1 International Fact-Checking Network (IFCN). (n.d.). Code of principles. Retrieved January 29, 2025, from https://www.ifcncodeofprinciples.poynter.org/ 2 European Fact-Checking Standards Network (EFCSN). (n.d.). 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1","display":"","copyAsset":false,"role":"figure","size":185086,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of Topics By Fact-Checking Model\u003c/p\u003e","description":"","filename":"Figure1PrevalenceofTopicsByFactCheckingModel.png","url":"https://assets-eu.researchsquare.com/files/rs-7421246/v1/6da4f1c2c6a5f11c9ddfc6d5.png"},{"id":92680048,"identity":"6a578fd8-3d18-4f44-9e68-88c8fee04faa","added_by":"auto","created_at":"2025-10-03 01:02:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184700,"visible":true,"origin":"","legend":"\u003cp\u003eComparing Claimants of Misinformation\u003c/p\u003e","description":"","filename":"Figure2.ComparingClaimantsofMisinformation.png","url":"https://assets-eu.researchsquare.com/files/rs-7421246/v1/cc150811cd27cb8cc810be7f.png"},{"id":92680054,"identity":"c2384d91-3225-45e9-8888-6f172f3a0994","added_by":"auto","created_at":"2025-10-03 01:02:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204386,"visible":true,"origin":"","legend":"\u003cp\u003eCitation Source Diversity by Model\u003c/p\u003e","description":"","filename":"Figure3.CitationSourceDiversitybyModel.png","url":"https://assets-eu.researchsquare.com/files/rs-7421246/v1/8d6525651ffc7179398e86ab.png"},{"id":106972117,"identity":"6ad59efc-4a0e-437d-b3f6-161328dd561f","added_by":"auto","created_at":"2026-04-15 10:22:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1453442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7421246/v1/4e3e3352-951f-4ea8-97a4-5698f4dd3cda.pdf"},{"id":92681636,"identity":"c98e111f-fc18-462e-ba9b-b20950861a4e","added_by":"auto","created_at":"2025-10-03 01:10:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":489261,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7421246/v1/d7c47d6835f46042fc3df5f8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fact-Checking Organisations in China: Characteristics, Verification Approaches, and Practices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFact-checking is the systematic, evidence-based evaluation of claims made by public actors, including politicians, journalists, and social media users (Graves \u0026amp; Amazeen, 2019; Vinhas \u0026amp; Bastos, 2022). It originated in Western liberal-democratic societies, where specialised organisations such as FactCheck.org (est. 2003) institutionalised the practice by issuing gradated verdicts like \u0026ldquo;true\u0026rdquo; or \u0026ldquo;misleading\u0026rdquo; (Graves, 2016; Cortada \u0026amp; Aspray, 2020; Amazeen, 2020). The 2010s marked a period of rapid global expansion, driven by concern over \u0026ldquo;fake news\u0026rdquo; and the proliferation of \u0026ldquo;alternative facts,\u0026rdquo; which undermined shared epistemic norms (Bennett \u0026amp; Livingston, 2020; Gordon, 2018; Cooke, 2018). In this context, fact-checking was redefined as both a safeguard for journalistic integrity and a corrective against democratic erosion.\u003c/p\u003e\n\u003cp\u003eThe institutionalisation of the field culminated in the establishment of transnational regulatory frameworks like the International Fact-Checking Network\u003csup\u003e1\u003c/sup\u003e (IFCN, 2015) and the European Fact-Checking Standards Network\u003csup\u003e2\u003c/sup\u003e (EFCSN, 2022), which codify principles such as neutrality, transparency, and ethical rigor (Beaudreau, 2024; Frau-Meigs \u0026amp; Corbu, 2024). By January 2025, the World Trends database of the United Nations Educational, Social and Cultural Organization (UNESCO) documented the existence of hundreds of nonpartisan fact-checking organisations around the world\u003csup\u003e3\u003c/sup\u003e. The IFCN has certified 155 signatory organisations across more than 60 countries, highlighting the field\u0026apos;s institutional maturation and transnational reach\u003csup\u003e4\u003c/sup\u003e. In addition to the aforementioned measures, the Duke University Reporters\u0026apos; Lab recorded 451 active fact-checking initiatives across 117 countries\u003csup\u003e5\u003c/sup\u003e. Platform collaborations and funding schemes, such as the Global Fact Check Fund\u003csup\u003e6\u003c/sup\u003e, have further augmented institutional capacity.\u003c/p\u003e\n\u003cp\u003eEmpirical studies demonstrate that fact-checking reduces public misperceptions, improves knowledge retention, and deters political actors from repeating falsehoods (Nyhan et al., 2020; Carey et al., 2022; Larraz et al., 2024). However, scholarly engagement remains heavily Western-centric. Most literature focuses on organisations such as PolitiFact, FactCheck.org, and Full Fact, analysing editorial routines and influence on public discourse (Graves, 2016; Walter et al., 2020; Graves \u0026amp; Cherubini, 2016). Country-level studies have centred on the US, UK, Spain, Germany, and Portugal (Cortada \u0026amp; Aspray, 2020; Cushion et al., 2022; Calvo et al., 2022), while comparative work explores variations in media trust and misinformation exposure across Western democracies (Mahl et al., 2024; Joux \u0026amp; Teixeira, 2024; L\u0026oacute;pez-Marcos \u0026amp; Vicente-Fern\u0026aacute;ndez, 2021; Humprecht, 2020).\u003c/p\u003e\n\u003cp\u003eThese studies often adopt Western media typologies\u0026mdash;liberal, democratic-corporatist, and polarised pluralist (Hallin \u0026amp; Mancini, 2004; Brandtzaeg et al., 2018; Cazzamatta, 2025a)\u0026mdash;which risk marginalising non-Western fact-checking as imitative or deficient. Scholarship on Asia, Africa, and Latin America remains limited (Lelo, 2024; Liu \u0026amp; Zhou, 2022; Cheruiyot \u0026amp; Ferrer-Conill, 2018), and few studies examine verification practices in hybrid or authoritarian systems (Cazzamatta, 2025b).\u003c/p\u003e\n\u003cp\u003eMainland China presents a salient yet under-theorised case in this regard. As of 2025, the Duke Reporters\u0026apos; Lab lists only six Chinese fact-checking initiatives\u0026mdash;five in Hong Kong and one in the mainland, none IFCN-certified. This absence reflects not inactivity, but rather China\u0026apos;s unique governance structures, where fact-checking is embedded within broader rumour-management systems operated by state-affiliated or commercial actors. Such hybrid configurations complicate comparisons with liberal-democratic models and demand new theoretical frameworks sensitive to authoritarian media logics and constrained civic space.\u003c/p\u003e\n\u003ch3\u003eThe Fact-Checking Movement in China\u003c/h3\u003e\n\u003cp\u003eThe fact-checking ecosystem of mainland China is characterised by institutional diversity, encompassing state media outlets, commercial platforms, academic research laboratories, and individual contributors. These initiatives have been developed in response to evolving policy imperatives, platform architectures, and information legitimacy crises. As early as 2011, People\u0026apos;s Daily initiated the \u0026quot;Qiu Zhen\u0026quot; (求真) column with the objective of combating online rumours\u003csup\u003e7\u003c/sup\u003e. In 2015, China\u0026apos;s criminal law was amended with a view to criminalising the deliberate spread of false information\u003csup\u003e8\u003c/sup\u003e. In that same year, Tencent launched \u0026quot;Jiao Zhen\u0026quot; (较真)\u003csup\u003e9\u003c/sup\u003e, which remains the only mainland-based fact-checking initiative listed by the Duke University Reporters\u0026apos; Lab (2025). In the academic sphere, Nanjing University initiated the \u0026quot;He Zhen Lu\u0026quot; (核真录) classroom-based verification project in 2017\u003csup\u003e10\u003c/sup\u003e. In 2018, the Cyberspace Administration of China and Xinhua News Agency established the Chinese Internet United Rumour-Debunking Platform (中国互联网联合辟谣平台)\u003csup\u003e11\u003c/sup\u003e. Subsequent to this, independent and professional initiatives were implemented: \u0026quot;China Fact Check\u0026quot; (有据, 2020)\u0026nbsp;\u003csup\u003e12\u003c/sup\u003efocuses on\u0026nbsp;international misinformation, while ThePaper launched \u0026quot;FactPaper\u0026quot; (澎湃明查) in 2021\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite their diversity, most initiatives operate within state-linked communication infrastructures, functioning more as public educators than adversarial watchdogs. Empirical scholarship remains limited. For instance, Fang (2021) examined the operations of China Fact Check using in-depth interviews. Liu and Zhou (2022), using a comparative content analysis, identified divergences between Jiao Zhen and the U.S.-based PolitiFact, particularly in claim types and transparency of sources. Zhang (2025), utilising interviews with FactPaper journalists, found that although methods and tools are disclosed, editorial decision-making is opaque and subject to institutional oversight.\u003c/p\u003e\n\u003cp\u003eOthers explore media effects: Chen and Neo (2024), using a 3\u0026times;3 between-subjects survey design, found that levels of government trust remained high even when misinformation was corrected via verified sources. Similarly, Wang et al. (2022) found that Chinese misinformation tends to concern lifestyle rather than politics. Methodologically, the field remains fragmented. While Zhang et al. (2024) and Hu et al. (2022) began constructing Chinese-language fact-checking corpora, comparative, large-scale datasets remain scarce.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOutside of China, the English-language commentary has emphasised China\u0026rsquo;s \u0026ldquo;rumour governance\u0026rdquo; paradigm and state-centric information control. Global media outlets and think tanks often frame Chinese fact-checking practices as extensions of narrative management. For example, Shin and Thorson (2017) argued that the professed commitment to truth is frequently undercut by instrumental political goals. Lam (2018) illustrated how authorities have historically labelled most unofficial information as \u0026ldquo;rumours.\u0026rdquo; While foreign observers may recognise certain localised innovations in China\u0026rsquo;s fact-checking practices, they frequently frame the country as an outlier instead of considering it a context where new institutional forms are emerging from within (Monta\u0026ntilde;a-Ni\u0026ntilde;o et al., 2024).\u003c/p\u003e\n\u003cp\u003eRecent scholarship notes that Chinese platforms have adapted Western verification models under political supervision (Quelle et al., 2025; Hokkanen, 2025), but most studies remain case-bound and lack typological synthesis. For instance, Wei (2024) and Zhang (2025) highlighted the influence of state ideology on editorial routines, these observations have rarely been embedded within a unified analytical framework. Meanwhile, a new cohort of individual fact-checkers on platforms like Douyin has emerged, relying on visual storytelling and participatory demonstrations (Lu \u0026amp; Shen, 2023), yet their practices are largely absent from academic accounts.\u003c/p\u003e\n\u003cp\u003eThis study addresses these gaps by developing a typological framework that examines Chinese fact-checking as a set of institutionalised communicative practices. Rather than treating China as a deviation from liberal-democratic standards, we conceptualise it as a distinct epistemic regime embedded in specific institutional and political logics.\u003c/p\u003e\n\u003ch3\u003eA Proposed Typology of Fact-Checking Models and Verification Cultures\u003c/h3\u003e\n\u003cp\u003eThis section develops an analytical framework for comparing Chinese fact-checking practices across three dimensions: organisational configuration, issue selection, and verification methodology. We first outline the primary institutional models found within China\u0026rsquo;s fact-checking landscape; next, we examine how each model dictates the selection of topics and accomplishes the verification of information. The section concludes with the formulation of the study\u0026rsquo;s three research questions.\u003c/p\u003e\n\u003ch4\u003e1). Organisational Configuration\u003c/h4\u003e\n\u003cp\u003eUnderstanding fact-checking as a culturally situated practice necessitates attention to its organisational embeddedness. Drawing on Knorr Cetina\u0026rsquo;s (2007) concept of \u0026ldquo;epistemic cultures\u0026rdquo; and Hanitzsch\u0026rsquo;s (2007) notion of \u0026ldquo;journalism cultures,\u0026rdquo; we conceptualise the organisational frame not as a static background but as a constitutive force that shapes how fact-checking is envisioned, legitimised, and operationalised (Luengo \u0026amp; Garc\u0026iacute;a-Mar\u0026iacute;n, 2020).\u003c/p\u003e\n\u003cp\u003eIn the Chinese context, attributes such as an entity\u0026rsquo;s founding history, institutional affiliation, staffing structure, funding logic, and declared mission are not trivial metadata\u0026mdash;they are integral to understanding the institutional logic of fact-checking. Rather than classifying organisations based solely on their legal status or funding source, this propesed framework treats these attributes as dynamic variables that influence normative orientations and editorial boundaries.\u003c/p\u003e\n\u003cp\u003eFor instance, organisational affiliation mediates editorial autonomy and public trust (Ceron et al., 2019); funding sources affect resource availability and perceived independence (Gras \u0026amp; Mendoza-Abarca, 2014); and staff backgrounds shape the epistemological assumptions underpinning verification standards (Godler \u0026amp; Reich, 2013). This perspective aligns with Graves\u0026rsquo; (2018) assertion that fact-checkers often operate within hybrid institutional environments, straddling the arenas of journalism, academia, and platform governance. By analytically foregrounding these organisational dimensions, we can better trace how institutional mandates materialise into fact-checking routines and how these routines reflect the broader norms of information governance (Koliska \u0026amp; Roberts, 2025). Indeed, in authoritarian or hybrid regimes such as China\u0026rsquo;s, these organisational configurations intersect with state logics of information control, requiring researchers to account for visible infrastructures as well as latent ideological alignments (Landerer, 2013).\u003c/p\u003e\n\u003ch4\u003e2). Issue Selection\u003c/h4\u003e\n\u003cp\u003eThe question of what is considered worth checking is far from neutral (Graves, 2016). Issue selection is a strategic and context-sensitive process that reflects and reproduces the institutional cultures and knowledge regimes in which fact-checkers operate. Building on Hardy (2021) and Grubert (2020), we conceptualise issue selection as the interface between organisational mandates and the evolving misinformation environment.\u003c/p\u003e\n\u003cp\u003eIn China\u0026rsquo;s case, this environment is shaped not only by audience demand and platform virality but also by regulatory red lines, political sensitivities, and algorithmic filtering. Our study treats issue selection as a cultural practice\u0026mdash;one that encodes values about public interest, legitimacy, and informational risk. The proposed typology enables a comparative analysis of how different models weigh these values.\u003c/p\u003e\n\u003cp\u003eAs Stocking and Gross (1989) have noted, the act of choosing what to verify inevitably implicates editorial bias, resource feasibility, and audience expectations. In China, such choices are further complicated by the need to avoid topics deemed politically sensitive or unverifiable under prevailing state information regimes (Liu \u0026amp; Zhou, 2022). Thus, analysing issue selection reveals how fact-checking organisations negotiate regulatory compliance, public trust, and editorial pragmatism. It also uncovers the adaptive strategies employed by different models, whether through topical specialisation, audience responsiveness, or reliance on user-submitted claims and platform analytics.\u003c/p\u003e\n\u003ch4\u003e3). Verification Methodology\u003c/h4\u003e\n\u003cp\u003eVerification lies at the core of fact-checking cultures. As Graves (2017) argued, verification is both a procedural and epistemological act: fact-checkers not only collect evidence but also structure truth claims in ways that resonate with their institutional logic and their audience\u0026rsquo;s expectations. In China, this process is shaped by state authority, technological platforms, and domain-specific norms.\u003c/p\u003e\n\u003cp\u003eThis study adopts a verification-centred approach to explore how truth is constructed across institutional contexts. Key dimensions\u0026mdash;such as claim accessibility, verdict style, visual evidence, and citation diversity\u0026mdash;serve as diagnostic indicators of what we term \u0026ldquo;verification cultures\u0026rdquo; (Kim et al., 2022; Wei, 2023).\u003c/p\u003e\n\u003cp\u003eIn Western contexts, fact-checkers often prioritise transparency through hyperlinking, source documentation, and multi-level verdicts (Typografia et al., 2024). However, as Nenno (2024) and Venturini (2019) have suggested, binary verdicts (true/false determinations) often fail to capture the complexity of real-world claims. This is particularly relevant in China, where preferred epistemic strategies vary across models. For instance, official fact-checkers may favour categorical outcomes to project institutional certainty, while academic initiatives might adopt narrative or interpretive styles that accommodate uncertainty or controversy.\u003c/p\u003e\n\u003cp\u003eStudying these strategies allows us to evaluate how verification serves different epistemic and communicative functions\u0026mdash;from maintaining political legitimacy to fostering civic education or facilitating user engagement. As Moreno Gil et al. (2022) noted, the legitimacy of verification depends not only on procedural rigour but also on contextual sensitivity\u0026mdash;a condition especially salient in politically mediated environments.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e4). Research Questions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformed by the preceding theoretical elaborations, this study is guided by three interrelated research questions:\u003c/p\u003e\n\u003cp\u003eRQ1: What organisational features characterise the fact-checking landscape in mainland China, and how do these features shape institutional norms and operational mandates?\u003c/p\u003e\n\u003cp\u003eRQ2: How do fact-checking organisations in China differ in their issue selection strategies, and what do these differences reveal about the interplay between institutional priorities and audience dynamics?\u003c/p\u003e\n\u003cp\u003eRQ3: How do information verification practices vary across different institutional models in China, and what epistemic assumptions underpin these variations?\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003e1. Country selection\u003c/h3\u003e\n\u003cp\u003eMainland China provides a theoretically generative yet under-theorised case in global fact-checking scholarship. Although absent from the IFCN signatory list, its verification landscape offers critical insight into how misinformation governance unfolds outside liberal-democratic settings. China\u0026apos;s fact-checking ecosystem is embedded in a distinctive nexus of state media governance, platform regulation, and constrained civic space, demanding new frameworks beyond Western-derived models such as the liberal or democratic-corporatist paradigms (Mahl et al., 2024).\u003c/p\u003e\n\u003cp\u003eFact-checking in China functions less as an independent watchdog and more as part of a broader \u0026ldquo;rumour governance\u0026rdquo; infrastructure. Initiatives span state media, commercial portals, university-based labs, and individual creators (e.g., Daddy Lab). This institutional heterogeneity reveals divergent verification logics, yet also underscores asymmetries in editorial autonomy and epistemic authority. State-affiliated models benefit from access to official sources but avoid politically sensitive topics, while individuals rely on platform visibility and focus on depoliticised content like health and consumer claims.\u003c/p\u003e\n\u003cp\u003eThese asymmetries highlight how institutional embedding shapes both what is verified and how verification is performed. The act of fact-checking reflects broader negotiations over truth, legitimacy, and communicative authority. Responding to recent calls to contextualise verification within sociotechnical environments (Zhang, 2025; Xiang \u0026amp; Neo, 2024), this study treats China not as a normative deviation but as a distinct epistemic regime. By tracing how different models operate under asymmetric regulatory conditions, we develop a typology that contributes to a more pluralistic theory of global verification cultures.\u003c/p\u003e\n\u003ch4\u003e2. Sample and Data Collection\u003c/h4\u003e\n\u003cp\u003eTo examine verification cultures across diverse organisational models in mainland China, we employed a purposive sampling strategy based on systematic keyword searches across national media platforms, social media databases, and prior empirical literature (Zhang et al., 2024). Entities were included if they had published at least 50 fact-checking outputs, either as written articles or annotated short videos. Based on these criteria, we selected ten fact-checking entities (comprising seven institutional actors and three prominent individual fact-checkers) representing a broad spectrum of organisational models (see Table 1).\u003c/p\u003e\n\u003cp\u003eThe selection process was not limited to specific platforms, such as China National Knowledge Infrastructure or Baidu, but incorporated triangulated data from media coverage, academic reports, and direct observations of platform activities. This broader scope ensured the inclusion of short video debunking, which is underrepresented in existing studies (Zhou, 2021; Zeng, 2021). While not exhaustive, the sample captured major institutional types, thematic domains, and verification styles, offering a valid basis for model-specific comparisons.\u003c/p\u003e\n\u003cp\u003eTable 1 Study Sample\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"107%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntity types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFact-checking articles/video texts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 3,428)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFact-checking articles\u003cbr\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 2,719)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual fact-checker video texts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll video texts\u003cbr\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 3,026)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFact-checking video texts\u003cbr\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 709)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eInstitutions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eQiu Zhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"7\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWen Zheng\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e12.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eChinese Internet United Rumour-Debunking Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e26.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJiao Zhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eFactPaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e14.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eChina Fact Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e15.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHe Zhen Lu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eIndividuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eFei Die Shuo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eXLab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eDaddy Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e15.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo compare the verification cultures, we constructed two datasets. First, we compiled organisational metadata from verified platform profiles and official websites, including the sites\u0026rsquo; affiliation type, staffing composition, funding model, and declared mission. These data were supplemented with academic sources to contextualise the institutional strategies and constraints. This dataset informed RQ1, which examined the organisational features of each model. Second, we constructed a corpus of fact-checking content, including 2,719 text-based articles produced by seven institutions between January 2022 and January 2025, obtained by using Python-based scraping tools. In parallel, we extracted 3,026 short video transcripts from the verified accounts of three individual fact-checkers (XLab, Daddy Lab, Fei Die Shuo) via Douyin\u003csup\u003e14\u003c/sup\u003e. Only content that explicitly debunked a specific claim was included, in line with standard definitions of fact-checking (Zhang et al., 2021).\u003c/p\u003e\n\u003cp\u003eTo ensure validity, a two-stage filtering process was employed, comprising (1) automated keyword matching (e.g., false, misleading, verified) and (2) manual annotation by two trained coders (Lu \u0026amp; Shen, 2023), who assessed whether each entry clearly addressed and corrected misinformation. A total of 709 short videos were retained, yielding an inter-coder reliability score of 89%. These datasets jointly support the analyses in RQ2 (issue selection) and RQ3 (verification practices).\u003c/p\u003e\n\u003ch4\u003e3. Measures and Analyses\u003c/h4\u003e\n\u003cp\u003eThis study employed a mixed-methods approach, integrating computational topic modelling, qualitative institutional analysis, and manual content coding to ensure methodological robustness and alignment with the three research questions.\u003c/p\u003e\n\u003cp\u003eTo address RQ1, we conducted a qualitative analysis of each entity\u0026rsquo;s institutional profile,\u0026nbsp;comprising\u0026nbsp;the following elements: founding year; affiliation type (e.g., official, commercial, academic, individual); declared mission; funding model (e.g., public budget, advertising, donation-based); and staffing background (e.g., journalism, academia, non-professionals). These features were extracted from the platform profiles and then cross-validated with academic and media sources (Liu \u0026amp; Zhou, 2022; Zhang et al., 2024; Yang et al., 2024). Institutional actors such as Qiu Zhen, Jiao Zhen, and Wen Zheng were compared with academic (He Zhen Lu) and individual (Daddy Lab) models to highlight divergences in editorial autonomy, scope, and legitimacy strategies.\u003c/p\u003e\n\u003cp\u003eTo address RQ2, we used BERTopic (v0.16.4) to conduct topic modelling of 2,719 articles and 709 video transcripts. We first applied Uniform Manifold Approximation and Projection (v0.5.7) for dimensionality reduction, followed by Hierarchical Density-Based Spatial Clustering of Applications with Noise (v0.8.4) for clustering. After removing 2.7% of noise points, we identified 237 topic clusters, which were manually reviewed and consolidated into 127 coherent topics and then grouped into 14 thematic clusters (e.g., domestic politics, education, technology, COVID-19, society). This process revealed model-specific editorial preferences and blind spots.\u003c/p\u003e\n\u003cp\u003eTo address RQ3, we performed a manual content analysis on a stratified sample of 400 items (40 items per entity) that were randomly drawn from the full corpus. A structured coding scheme was developed based on prior literature (Vu et al., 2023; Mahl et al., 2024). The coding variables comprised the following six elements: (1) claim accessibility (0 = not provided, 1 = directly provided, 2 = indirectly provided); (2) verdict style (0 = none, 1 = categorical, 2 = narrative); (3) claimant type (e.g., social media user, government official); (4) use of visuals (0 = none, 1 = present); (5) citation of sources (e.g., official reports); and (6) source diversity (0 = none, 1 = one source type, 2 = two or more source types). Inter-coder reliability was tested on a 10% subsample (n = 40), yielding Krippendorff\u0026rsquo;s \u0026alpha; \u0026ge; 0.89 for all variables. This manual layer enabled the close analysis of the verification forms, degrees of transparency, and underlying epistemic grammars. Together, the triangulated analyses produced a granular map of verification cultures, clarifying how each model constructs credibility, authority, and trustworthiness.\u003c/p\u003e"},{"header":"Empirical Findings: Model-Specific Patterns in Issue Selection and Verification Practices","content":"\u003cp\u003eThis section presents the empirical findings in alignment with the three research questions, using a typological comparison across the ten sampled fact-checking entities. For additional institutional details, see Supplementary Material Tables B and C.\u003c/p\u003e\n\u003ch4\u003e1. Fact-Checkers\u0026apos; Organisational Configurations\u003c/h4\u003e\n\u003cp\u003eAddressing RQ1, the organisational landscape of Chinese fact-checking entities can be categorised into four institutional models: the official model (public-oriented), the professional/commercial model (market-oriented), the independent/academic model (non-profit-oriented), and the individual fact-checking model (autonomy-oriented). Each model demonstrates unique characteristics in terms of its affiliation, staffing, funding mechanisms, and mission statements (see Table 2).\u003c/p\u003e\n\u003cp\u003eThe official model includes Qiu Zhen, Wen Zheng, and the Chinese Internet United Rumour-Debunking Platform. These entities are affiliated with central government agencies or state-owned media and serve authoritative roles in rumour regulation and information dissemination. They are typically staffed by trained journalists with backgrounds in political communication, and they are funded through institutional or public budgets, which shield them from commercial pressures. Thematically, they focus on domestic politics, COVID-19, and public security, aligning with state priorities in crisis communication and policy clarification.\u003c/p\u003e\n\u003cp\u003eThe professional/commercial model comprises Jiao Zhen and FactPaper, which are part of major digital media conglomerates. These organisations follow market-oriented logics, monetising through advertising and platform partnerships. Their editorial teams often combine journalism, platform strategy, and data analytics. They prioritise high-traffic topics such as scams, health misinformation, international affairs, and lifestyle content, with themes such as COVID-19, the US, and international politics dominating their coverage.\u003c/p\u003e\n\u003cp\u003eThe independent/academic model comprises China Fact Check and He Zhen Lu. These are non-commercial entities that are either fully independent or university-affiliated. He Zhen Lu operates as a classroom-based academic initiative, while China Fact Check functions as a charitable project targeting international misinformation in Chinese. Both enjoy relatively high editorial autonomy and tend to focus on international news, social controversies, and critical reviews of Western media narratives.\u003c/p\u003e\n\u003cp\u003eThe individual fact-checking model comprises Daddy Lab, Fei Die Shuo, and XLab\u0026mdash;short video-based creators that are active primarily on Douyin. Their content is visual, platform-optimised, and highly interactive. These creators focus on product testing, science education, and health claims, often using participatory demonstrations. While not professionally trained in journalism, the creators frequently possess domain-specific expertise (e.g., chemistry, engineering). Their funding depends on platform incentives and commercial sponsorship, which can create tensions between popularity and epistemic rigour.\u003c/p\u003e\n\u003cp\u003eDespite their differences, all four models exhibit shared commitments to accuracy and procedural consistency, although a formal affiliation with global standards (e.g., the IFCN) remains rare due to China\u0026apos;s distinct governance structures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Organisational Configurations of Fact-Checking Models in China\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1071\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLauch time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffiliation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStaff backgrounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunding mechanisms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eQiu Zhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eOfficial platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eOfficial model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePeople\u0026apos;s Daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eProfessional journalists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePublic financial allocations and institutional budgets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eMaintain information authenticity, combat online rumours, and safeguard public trust in government and media.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eWen Zheng\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eOfficial platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eXinhua News Agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eProfessional journalists\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eChinese Internet United Rumour-Debunking Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eOfficial platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eCyberspace Administration of China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eProfessional journalists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eRelease authoritative information to dispel rumours, enhance netizens\u0026apos; media literacy, and create a clear and healthy online environment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eJiao Zhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCommercial platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eProfessional/commercial model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eTencent News\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eProfessional journalists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eDiversified funding model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eCheck the real and fake news in daily life.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eFactPaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eProfessional platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eThePaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eProfessional journalists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eBuild a global fact-checking platform that is more comprehensive, professional, open, and interactive, serving as a \u0026ldquo;calibrator\u0026rdquo; for global hot topics and public events.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eChina Fact Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eIndependent groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eIndependent/academic model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNonprofit independent start-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMultidisciplinary team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePurely charitable project with no external investment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eFact-check international news in Chinese.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eHe Zhen Lu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAcademic groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNanjing University\u0026apos;s School of Journalism and Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eProfessors and students from the School of Journalism and Communication at Nanjing University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eReliance on colleges and academic partnerships for funding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eEvaluate the accuracy of factual statements in Chinese media news reports.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eFei Die Shuo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eIndividual fact-checkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eIndividual fact-checking model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eIndependent start-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eNon-professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eDiversified funding model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eProduct testing and health claims verification.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eXLab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eIndividual fact-checkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eIndependent start-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eNon-professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eDebunk science and health misinformation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDaddy Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eIndividual fact-checkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eIndependent start-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eNon-professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eDebunk tech myths and misinformation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e2). Fact-Checkers\u0026apos; Issue Selection\u003c/h4\u003e\n\u003cp\u003eAddressing RQ2, the topic modelling and qualitative comparison revealed distinct editorial priorities shaped by institutional logics (see Figure 1 and Supplementary Table B).\u003c/p\u003e\n\u003cp\u003e2.1 Thematic Priorities Across Organisational Models\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFact-checking topics in China span the political, social, economic, health, and international domains, with notable divergences across the four institutional models. Each model reflects specific institutional mandates and audience orientations, resulting in divergent topical emphases.\u003c/p\u003e\n\u003cp\u003eThe official model places considerable emphasis on domestic politics (30.07%), public security and military (13.06%), and COVID-19 (12.99%), underscoring its core mandate to preserve political legitimacy and social stability. It also addresses society (8.06%), justice and crime (7.22%), and food and health (5.97%), consistent with state-led priorities in crisis communication and public administration. Topics related to Xinjiang and Tibet are visibly present, reflecting this model\u0026rsquo;s role in safeguarding the narratives concerning national unity. In contrast, there is minimal engagement with technology (0.69%), the US (1.53%), and Hong Kong/Macau (0.14%), indicating a cautious stance toward internationally sensitive themes.\u003c/p\u003e\n\u003cp\u003eThe professional/commercial model prioritises high-visibility, viral topics, including international politics (35.13%), COVID-19 (13.03%), and the US (11.47%). Additional focus areas include domestic politics (10.91%) and Hong Kong/Macau (7.93%), highlighting this model\u0026rsquo;s responsiveness to geopolitical controversies and audience curiosity. Cross-strait issues frequently appear, reflecting this model\u0026rsquo;s market-driven emphasis on high-engagement, politically charged narratives.\u003c/p\u003e\n\u003cp\u003eThe independent/academic model shares some similarities with the professional/commercial model\u0026mdash;especially regarding international politics (33.86%) and the US (16.06%), but key distinctions emerge upon closer analysis. These actors prioritise Asia-related topics and focus their U.S. coverage on public figures such as Donald Trump and Elon Musk. The model is characterised by its attention to transnational misinformation, critical monitoring of Western media narratives, and value-laden controversies. These include women\u0026rsquo;s rights (6.38%), cross-strait relations (4.32%), and military activity (6.57%). Additionally, this model engages with the topics of justice and crime (4.01%), public transportation (4.54%), and environment and climate change (3.84%), typically through a critical epistemological lens.\u003c/p\u003e\n\u003cp\u003eThe individual fact-checking model demonstrates a grassroots orientation, with topical emphases on economy (27.08%), food and health (23.70%), education (16.50%), and society (14.81%). This reflects a focus on everyday concerns, including product safety, dietary practices, holiday customs, and family life, illustrating this model\u0026rsquo;s commitment to audience engagement and practical relevance.\u003c/p\u003e\n\u003cp\u003eAcross all of the models, some topics demonstrate cross-cutting significance. For instance, justice and crime, public transportation, and environment and climate change are relatively well-covered by the official model (7.22%, 4.58%, 3.89%), the professional/commercial model (3.68%, 3.12%, 2.97%), and the independent/academic model (4.01%, 4.54%, 3.84%), indicating a shared institutional interest in infrastructural and regulatory concerns. Conversely, cross-strait relations appear predominantly in the professional and academic models, while Hong Kong/Macau-related misinformation is addressed almost exclusively by the professional/commercial model (7.93%). These thematic distributions highlight not only divergent editorial logics but also the structural constraints and ideological positions that shape issue selection across China\u0026rsquo;s fact-checking ecosystem.\u003c/p\u003e\n\u003cp\u003e2.2 Sub-Thematic Variations and Cross-Model Dynamics\u003c/p\u003e\n\u003cp\u003eSignificant intra-cluster differences further distinguish the fact-checking models within shared thematic categories (see Supplementary Table B). Within the broad category of \u0026ldquo;society\u0026rdquo;\u0026mdash;covered by the official (8.06%), professional/commercial (0.71%), independent/academic (10.82%), and individual (14.81%) models\u0026mdash;each demonstrates distinct sub-thematic orientations, shaped by the institutional identity and communicative strategy.\u003c/p\u003e\n\u003cp\u003eThe official model primarily targets misinformation related to social unrest, patriotic mobilisation, major public events, sensational domestic incidents, and elderly care policies. This aligns with the state\u0026rsquo;s interest in reinforcing stability narratives and clarifying contentious mass incidents or ideological interpretations. The professional/commercial model exhibits a preference for highly relatable and socially resonant issues, such as public opinion manipulation, matchmaking and dating culture, overtime work disputes, and labour rights. These topics are frequently selected based on their virality and capacity to engage younger, urban online audiences, reflecting the model\u0026rsquo;s responsiveness to trending social discourse. The independent/academic model engages deeply with value-laden controversies, focusing on media manipulation, gender equity, rights of marginalised groups, and ethically contentious issues such as surrogacy. This approach reflects a more critical and epistemologically driven orientation. The individual fact-checking model emphasises everyday relevance, focusing on misinformation related to product safety, reproductive and women\u0026rsquo;s rights, traditional Chinese behavioural norms, festive customs, and family-centred social practices such as the Spring Festival travel season. These creators employ accessible narratives and culturally resonant formats to debunk social myths and clarify distorted popular beliefs.\u003c/p\u003e\n\u003cp\u003eThese sub-thematic distinctions not only reflect divergent editorial strategies but also reveal the varying degrees of institutional risk tolerance and discursive ambition across the models. Together, these patterns delineate a differentiated yet interdependent ecosystem of issue selection in the contemporary Chinese fact-checking field.\u003c/p\u003e\n\u003cp\u003e2.3 Issue Selection Practices and Criteria\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComplementing the quantitative analysis, insights from news reports and policy documents shed light on how the different models operationalise issue selection.\u003c/p\u003e\n\u003cp\u003eProfessional/commercial platforms (e.g., FactPaper) do not adhere to formal topic frameworks. Instead, they prioritise real-time responsiveness to trending misinformation, especially regarding global hotspots, viral rumours, and geopolitical controversies. Notably, FactPaper emphasises tracking foreign-origin falsehoods, particularly those shaping global perceptions of China (FactPaper, 2023). The official model follows explicit editorial mandates prioritising political stability, national unity, and crisis control, often selecting topics that align with state-defined ideological boundaries and communication goals (People\u0026apos;s Daily, 2023). Independent/academic fact-checkers pursue diversification strategies to maintain public trust and avoid perceived bias. China Fact Check, for instance, engages with international rumours within Sinophone contexts, balancing analytical rigour with reputational neutrality. These actors often frame their selection as both a professional duty and a knowledge-building endeavour, emphasising transparency in methods and source credibility. The individual fact-checking model demonstrates a distinct focus on practical misinformation affecting daily life, such as consumer safety, health tips, and culturally specific claims, including common misinterpretations of festivals or traditional customs (Lu \u0026amp; Shen, 2023). This model\u0026rsquo;s prioritisation is driven largely by audience feedback, topical resonance, and platform-specific engagement metrics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll of the models utilise multi-channel discovery tools, including keyword alerts, platform analytics, user submissions, and proprietary monitoring systems. However, coverage of offline misinformation\u0026mdash;such as political rumours or grassroots protests\u0026mdash;remains limited, indicating a digital-trace dependency and low-risk orientation. Despite their operational differences, the fact-checkers across all four of the models apply shared selection criteria: assessing a claim\u0026rsquo;s social harm, potential for virality, and verifiability. Claims that are opinion-based, satirical, or unverifiable (e.g., conspiracy theories) are routinely excluded. This methodological discipline aligns with international standards, where falsifiability is essential for claim eligibility (He Zhen Lu, 2024). Ultimately, issue selection is shaped by each model\u0026rsquo;s institutional identity, epistemic stance, and audience engagement logic. Official actors act as the guardians of political order; professional/commercial platforms function as trend-sensitive communicators; independent/academic groups operate as critical analysts; and individual fact-checkers work as community-based myth debunkers. Although thematic overlaps exist, the practices are filtered through distinct mandates and constraints, producing a multi-scalar, pluralistic fact-checking environment.\u003c/p\u003e\n\u003ch4\u003e3). Fact-Checkers\u0026apos; Information Verification\u003c/h4\u003e\n\u003cp\u003eIn response to RQ3, the manual content analysis revealed systematic differences in the verification performance across the four models (see Table 3; Figures 2 and 3; Supplementary Table C; Figures A and B).\u003c/p\u003e\n\u003cp\u003eTable 3 Comparison of Accessibility, Type of Verdict, Visual Elements Used, and Number of Resource Types\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOfficial\u003cbr\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 1440 articles)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfessional/Commercial\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 706 articles)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent/Academic\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 573 articles)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual Fact-Checking\u003cbr\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 709 video texts)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQiu Zhen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWen Zheng\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChinese Internet United Rumour-Debunking Platform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJiao Zhen\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactPaper\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina Fact Check\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHe Zhen Lu\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFei Die Shuo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eXLab\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaddy Lab\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003enot\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eprovide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003cp\u003e(40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003edirect provide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003cp\u003e(52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e(58.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e(63.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e(26.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eindirect provide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e(36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e(38.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e(36.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e(33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVerdict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eno\u0026nbsp;\u003c/p\u003e\n \u003cp\u003everdict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003edefinitive verdict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003cp\u003e(87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003cp\u003e(82.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003cp\u003e(70.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003enarrative verdict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e(29.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisuals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003cp\u003e(75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003cp\u003e(82.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003cp\u003e(98.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResource\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eno source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eone type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003cp\u003e(70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e(30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003cp\u003e(70.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003etwo or\u003c/p\u003e\n \u003cp\u003emore types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003cp\u003e(30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e(70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003cp\u003e(87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e(29.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.1 Accessibility of Claims\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClaim accessibility varies significantly across the models, and it is shaped by both platform affordances and institutional constraints. The official (10.9%) and individual (40%) models show the highest proportion of fact-checks lacking direct access to original claims.\u003c/p\u003e\n\u003cp\u003eOfficial fact-checkers frequently summarise their claims using concise, declarative formulations, relying on institutional authority and public trust to legitimise their assessments without linking to source content. In contrast, individual fact-checkers often paraphrase claims, cite anonymous sources, or omit direct references, due to platform restrictions (e.g., algorithmic moderation, link suppression) and the structural limitations of short-form video formats.\u003c/p\u003e\n\u003cp\u003eBy comparison, the professional/commercial (58.75%) and independent/academic (63.75%) models demonstrate a strong preference for direct accessibility\u0026mdash;including screenshots, hyperlinks, and timestamped references\u0026mdash;thereby reinforcing procedural transparency.\u003c/p\u003e\n\u003cp\u003e3.2 Verdict Types: Categorical Clarity vs. Narrative Nuance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCategorical verdicts dominate China\u0026rsquo;s fact-checking landscape. The official model applies definitive labels in 100% of the cases, while the professional/commercial (87.5%) and independent/academic (82.5%) models also favour this approach. Common verdicts include \u0026quot;false,\u0026quot; \u0026quot;misleading,\u0026quot; or \u0026quot;true,\u0026quot; often presented with icons or colour-coded labels to enhance user comprehension.\u003c/p\u003e\n\u003cp\u003eThe official model uses these unambiguous verdicts to assert its institutional legitimacy and ensure clarity in politically sensitive settings. Professional/commercial platforms also benefit from categorical verdicts, particularly for enhancing readability on mobile interfaces.\u003c/p\u003e\n\u003cp\u003eIn contrast, the individual model uses categorical verdicts in only 70.83% of cases. A substantial 29.17% of its assessments take a narrative form, using process-based explanations or demonstrations. This reflects an educational orientation focused on the audiences\u0026rsquo; understanding of the material. Similarly, independent/academic actors issue narrative verdicts in 20% of cases, particularly when dealing with scientific uncertainty or regulatory ambiguity.\u003c/p\u003e\n\u003cp\u003e3.3 Claimants and Targeted Scrutiny\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 2, social media users are the most frequently identified claimants across all models: official (80.8%), professional/commercial (77.5%), and independent/academic (66.25%). This trend highlights a collective concern with viral misinformation originates from grassroots platforms, rather than from political elites or institutions.\u003c/p\u003e\n\u003cp\u003eFor the official model, this focus supports its mission of managing public discourse and mitigating bottom-up disruptions. Professional/commercial platforms, although similarly focused, present a broader claimant profile, influenced by audience attention and platform metrics.\u003c/p\u003e\n\u003cp\u003eBy contrast, the individual model shows the most diversified profile: In that group, 56.67% of claims are attributed to social media users, while 12.5% originate from the science and research sector, 10.83% from healthcare institutions, and 7.5% from the civil sector. This reflects the model\u0026rsquo;s grassroots positioning and domain-specific responsiveness.\u003c/p\u003e\n\u003cp\u003eThe professional/commercial and academic models are also more likely to challenge institutional or transnational sources, including traditional media (3.75%), new media (8.75% and 15%), and international political actors (6.25% and 3.75%). This reflects a relatively higher degree of editorial independence and a willingness to contest elite or foreign narratives, a flexibility not afforded to official actors, who avoid such scrutiny for political reasons.\u003c/p\u003e\n\u003cp\u003e3.4 Visual and Evidentiary Support\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll of the models rely on visual material, but the form and purpose vary. Individual fact-checkers use visuals in 100% of cases, including comparative product tests, infographics, and animated explainers optimised for short videos.\u003c/p\u003e\n\u003cp\u003eIndependent/academic models (98.75%) frequently use screenshots from videos, news sites, or official sources, particularly in addressing media manipulation and transnational misinformation. Professional/commercial platforms (82.5%) favour infographic storytelling and data visualisation, particularly when addressing public health or technology topics.\u003c/p\u003e\n\u003cp\u003eIn contrast, the official model (75%) primarily employs government-issued documents, policy papers, or legal records to affirm institutional authority. These visuals serve a declarative function rather than offering an interpretive analysis.\u003c/p\u003e\n\u003cp\u003e3.5 Source Citation and Epistemic Hierarchy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 3, citation practices reflect each model\u0026rsquo;s epistemic orientation and knowledge hierarchy. The official model relies predominantly on government departments (54%), public security agencies (47%), and traditional media (33%), illustrating a top-down model that is grounded in state epistemology. Professional/commercial platforms combine official (18%) and traditional (33%) media sources, with an increasing utilisation of international governmental data (13%) and medical institutions (40%), constructing hybrid evidentiary chains suited to a mass audience with broad topical interests. Independent/academic models exhibit the highest reliance on academic literature and in-house resources (57%), supplemented by diverse external references\u0026mdash;such as international governments (27%) and traditional media (48%)\u0026mdash;underscoring their epistemic autonomy and methodological stringency. In contrast, individual fact-checkers prioritise empirical and scientific sources (39%), followed by health authorities (21%), civil knowledge (18%), and social media (18%), emphasising hands-on validation and experiential expertise.\u003c/p\u003e\n\u003cp\u003eThese stratifications are further evidenced in source diversity: 70% of outputs by the official and individual models cite only one type of source, while the professional/commercial (70%) and academic (87.5%) models routinely draw from multiple types. These disparities shape not only the robustness of the verification but also its perceived legitimacy.\u003c/p\u003e\n\u003cp\u003e3.6 Practices and Challenges in Verification\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll models employ tools such as reverse image searches, keyword monitoring, and geolocation to trace misinformation provenance. However, engagement with original claimants remains rare due to potential reputational or legal risks. Large-scale platforms (e.g., Tencent, ThePaper) provide limited institutional support (such as legal or psychological counselling), and independent creators often lack formal protective mechanisms, underscoring the systemic vulnerabilities within China\u0026apos;s verification ecosystem.\u003c/p\u003e\n\u003cp\u003eOn epistemological grounds, official and commercial entities prefer categorical verdicts, facilitating communicative clarity under time constraints. Academic models delay or refrain from issuing judgements in uncertain cases, preserving rigour over reach. Individual fact-checkers compensate for lower institutional legitimacy by crowdsourcing evidence, inviting user input, and offering transparent testing processes, thereby fostering participatory verification cultures.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the verification practices of China\u0026rsquo;s fact-checking models are not merely technical operations but expressions of institutional logic, resource environments, and public trust frameworks. Official actors anchor legitimacy in the state, commercial platforms in visibility and speed, academics in epistemic rigour, and individuals in credibility-through-practice. These dynamics illustrate that verification in China is both a methodological discipline and a culturally embedded communicative act, negotiating contested terrains of authority, evidence, and truth.\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch3\u003e\u003cem\u003e1). Fact-Checking Cultures: Areas of Divergence and Convergence\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eAt the national level, China\u0026rsquo;s fact-checking field demonstrates a form of institutional pluralism operating within a unifying logic of political containment and epistemic control. Rather than existing in outright conflict, the four fact-checking models coalesce around a broader architecture shaped by China\u0026apos;s state-led information order. Official actors such as the Chinese Internet United Rumour-Debunking Platform exemplify state-integrated epistemic infrastructures in which verification is a communicative function of governance. However, even independent and academic initiatives, while discursively distinct, often navigate implicit boundaries that discourage confrontation with politically sensitive claims. These institutionalised boundaries do not merely restrict content; they normalise selective silence, cultivating a field where public trust is managed rather than negotiated. This system-wide orientation suggests that China\u0026apos;s fact-checking field serves as a regulatory mechanism of controlled transparency, upholding civic informational without compromising state sovereignty.\u003c/p\u003e\n\u003cp\u003eAt the model level, divergences in institutional affiliation, editorial practice, and verification logic manifest in distinct modes of truth performance. The official model operationalises verification as a bureaucratic mandate: Verdicts are categorical, claim sources are often unspecified, and citations overwhelmingly favour state-sanctioned data. This reaffirms institutional authority while limiting epistemic diversity. In contrast, commercial platforms adopt audience-centric responsiveness, employing real-time monitoring tools and diversified sources to maintain virality and credibility. Their output often blends professional routines with algorithmic optimisation, producing highly visual, mobile-optimised content that favours engagement over investigative depth. Independent/academic initiatives are unique in that they emphasise procedural integrity and international orientation. Their frequent use of narrative verdicts, multi-source triangulation, and critical discourse positions them as epistemic boundary agents, expanding verification from declarative truth assessment to reflective knowledge critique. Meanwhile, individual fact-checkers, although structurally marginal, inject experiential authority into the verification landscape. Their pragmatic focus on consumer safety, product testing, and health misinformation leverages platform logics to cultivate trust through embodied evidence and public accessibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these differences, convergent patterns are observable. Most of the models demonstrate growing adherence to internationally recognised fact-checking norms: transparency in verdicts, source citation, and multimodal evidence presentation. These convergences reflect not only methodological maturation but also a subtle process of institutional mimicry, where newer actors emulate the routines, aesthetics, and normative postures of their more established peers. This mimetic isomorphism, driven by shared environmental pressures (e.g., platform regulation, user expectations, credibility benchmarks), is reshaping the field into a functionally differentiated yet stylistically aligned verification ecosystem.\u003c/p\u003e\n\u003cp\u003eHowever, this convergence has epistemic costs. The avoidance of sensitive domains\u0026mdash;such as corruption, religion, or ethnic issues\u0026mdash;indicates a tacit editorial consensus across the models. This is less a matter of legal censorship than of strategic self-limitation and reputational risk management. Academic and independent groups, although rhetorically committed to pluralism, often mirror official risk-avoidance strategies to maintain their access, visibility, and survival. Thus, what appears as diversity in operational form masks deeper conformity in issue avoidance\u0026mdash;a form of epistemic convergence shaped by regulatory habituation rather than direct coercion.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has demonstrated that fact-checking in mainland China should be understood not merely as a response to misinformation but also as a socially embedded, institutionally mediated mode of epistemic governance. Drawing from a tripartite framework\u0026mdash;focusing on organisational logics, issue selection, and verification practices\u0026mdash;we reconceptualise fact-checking as a cultural form of boundary work. Rather than functioning as a neutral or adversarial truth regime, fact-checking in China operates through a complex interplay of authority, constraint, and adaptation. Across four institutional models (official, professional/commercial, independent/academic, and individual), verification practices reflect both functional differentiation and normative alignment. These patterns emerge not from policy coercion alone but through institutional emulation, reputational calibration, and negotiated legitimacy within a state-integrated media environment.\u003c/p\u003e\n\u003cp\u003eConceptually, this research contributes to the theorisation of fact-checking cultures by revealing how verification practices mediate between institutional mandates and public expectations. We have demonstrated that verification is not just a technical process of claim adjudication\u0026mdash;it is a performative act through which actors define epistemic legitimacy, allocate evidentiary weight, and navigate normative boundaries. The models differ in operational scope as well as in how they frame truth\u0026mdash;through categorical judgement, narrative reasoning, or visual demonstration\u0026mdash;each encoding distinct assumptions about authority, audience, and credibility. By foregrounding these epistemic grammars, this study advances a situated understanding of how truth claims are constructed, authorised, and circulated in hybrid politically systems.\u003c/p\u003e\n\u003cp\u003eMethodologically, the study illustrates the analytical power of mixed-methods research in capturing both structural patterns and contextual nuance. By integrating computational topic modelling with stratified content analysis, we have mapped thematic diversity while tracing micro-level verification logics. The result is a typological portrait that respects institutional specificity without reducing complexity to binaries of state-versus-market or control-versus-resistance. The sampling strategy, grounded in operational output rather than institutional status, also opens new possibilities for comparative studies across fragmented, platform-governed media ecosystems.\u003c/p\u003e\n\u003cp\u003eLooking ahead, the framework developed here invites further research on how verification cultures emerge, adapt, or dissolve under shifting technological and political conditions. Cross-national comparisons\u0026mdash;particularly among Global South societies or in authoritarian-capitalist contexts\u0026mdash;may uncover alternative models of epistemic labour, including grassroots, adversarial, and transnational forms.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical considerations\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contribution statement\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author 1: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeclaration of conflicting interest\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding statement\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed receipt of financial support for the research, authorship, and/or publication of this article from a regional research center (details redacted for peer review).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data used in this study are drawn from publicly available news and video sources. \u0026nbsp;\u003c/p\u003e"},{"header":"Notes","content":"\u003cp\u003e1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;International Fact-Checking Network (IFCN). (n.d.). Code of principles. Retrieved January 29, 2025, from https://www.ifcncodeofprinciples.poynter.org/\u003c/p\u003e\n\u003cp\u003e2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;European Fact-Checking Standards Network (EFCSN). (n.d.). Code of standards. Retrieved January 29, 2025, from https://efcsn.com/code-of-standards/\u003c/p\u003e\n\u003cp\u003e3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;UNESCO. (n.d.). World trends database on global fact-checking sites. Retrieved January 29, 2025, from https://www.unesco.org/en/world-media-trends/global-fact-checking-sites\u003c/p\u003e\n\u003cp\u003e4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;International Fact-Checking Network (IFCN). (n.d.). Certified signatory organizations list. Retrieved January 29, 2025, from https://www.ifcncodeofprinciples.poynter.org/signatories\u003c/p\u003e\n\u003cp\u003e5\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Duke University Reporters\u0026rsquo; Lab. (n.d.). Global fact-checking project database. Retrieved January 29, 2025, from https://reporterslab.org/fact-checking/\u003c/p\u003e\n\u003cp\u003e6\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Poynter Institute. (n.d.). Global fact check fund. Retrieved January 29, 2025, from https://www.poynter.org/ifcn/grants-ifcn/globalfactcheckfund/\u003c/p\u003e\n\u003cp\u003e7 People\u0026rsquo;s Daily. (2024).\u0026nbsp;人民网\u0026ldquo;求真\u0026rdquo;栏目\u0026nbsp;[\u0026ldquo;Qiu Zhen\u0026rdquo;\u0026nbsp;section of People.cn]. Retrieved January 29, 2025, from http://society.people.com.cn/GB/229589/index1.html [in Chinese]\u003c/p\u003e\n\u003cp\u003e8\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National People\u0026apos;s Congress. (2015). Ninth amendment to the PRC criminal law. Retrieved January 29, 2025, from http://www.npc.gov.cn/npc/xinwen/2015-08/31/content_1945587.htm [in Chinese]; English version available via Westlaw China (subscription required).\u003c/p\u003e\n\u003cp\u003e9 Tencent. (2024).\u0026nbsp;较真内容原则\u0026nbsp;[Content principles of\u0026nbsp;\u0026ldquo;Jiao Zhen\u0026rdquo;]. Retrieved January 29, 2025, from https://vp.fact.qq.com/ssrtruth [in Chinese]\u003c/p\u003e\n\u003cp\u003e10 Nanjing University. (2024).\u0026nbsp;核真录实践平台\u0026nbsp;[He Zhen Lu practice platform]. Retrieved January 29, 2025, from https://media.nju.edu.cn/sjpt/list.htm [in Chinese]\u003c/p\u003e\n\u003cp\u003e11 Cyberspace Administration of China. (2024).\u0026nbsp;中国互联网联合辟谣平台\u0026nbsp;[China internet joint rumor refutation platform]. Retrieved January 29, 2025, from http://www.piyao.org.cn/ [in Chinese]\u003c/p\u003e\n\u003cp\u003e12 Wei, X. (2023, April).\u0026nbsp;事实核查手册\u0026nbsp;[Fact-checking manual]. Retrieved January 29, 2025, from https://chinafactcheck.com/wp-content/themes/youju/assets/fact-check-manua-PC.pdf [in Chinese]\u003c/p\u003e\n\u003cp\u003e13 ThePaper. (2024).\u0026nbsp;澎湃明查平台\u0026nbsp;[Pengpai Mingcha platform]. Retrieved January 29, 2025, from https://www.factpaper.cn/large [in Chinese]\u003c/p\u003e\n\u003cp\u003e14 During data collection, three individual fact-checkers were found to have published 1,061 videos between January 2022 and January 2025. However, the number of scripts that met the selection criteria was limited. To ensure sufficient analytical material, all videos posted since account creation were reviewed, and relevant ones were selected, yielding 709 fact-checking-related videos.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmazeen, M. A. (2020). Journalistic interventions: The structural factors affecting the global emergence of fact-checking. Journalism, 21(1), 95\u0026ndash;111. https://doi.org/10.1177/1464884917730217\u003c/li\u003e\n \u003cli\u003eBeaudreau, J. (2024). The political economy of fact-checking: From hope to reality check. In Disinformation Debunked (pp. 37\u0026ndash;66). Routledge.\u003c/li\u003e\n \u003cli\u003eBennett, W., \u0026amp; Livingston, S. (2020). The disinformation age. Cambridge University Press.\u003c/li\u003e\n \u003cli\u003eBrandtzaeg, P. B., F\u0026oslash;lstad, A., \u0026amp; Chaparro Dom\u0026iacute;nguez, M. \u0026Aacute;. (2018). 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Political Behavior, 42, 939\u0026ndash;960.\u003c/li\u003e\n \u003cli\u003eOiwan Lam. (2018). \u0026lsquo;Fake news\u0026rsquo; is in the eye of the beholder: China is centralizing efforts to stop online \u0026lsquo;rumors\u0026rsquo;. StopFake.org. Retrieved from https://www.stopfake.org/en/fake-news-is-in-the-eye-of-the-beholder-china-is-centralizing-efforts-to-stop-online-rumors/\u003c/li\u003e\n \u003cli\u003eQuelle, D., Cheng, C. Y., Bovet, A., \u0026amp; Hale, S. A. (2025). Lost in translation: Using global fact-checks to measure multilingual misinformation prevalence, spread, and evolution. EPJ Data Science, 14(1), 22.\u003c/li\u003e\n \u003cli\u003eSch\u0026auml;fer, K., Choi, J. E., Vogel, I., \u0026amp; Steinebach, M. (2024). Unveiling the potential of BERTopic for multilingual fake news analysis\u0026mdash;Use case: COVID-19. https://doi.org/10.48550/arXiv.2407.08417\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShin, J., \u0026amp; Thorson, K. (2017). 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The case of Chinese. https://doi.org/10.48550/arXiv.2401.15498\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang, F., Liu, J., Xie, J., Zhang, Q., Xu, Y., \u0026amp; Zha, Z. J. (2024, May). EscNet: Entity-enhanced and stance checking network for multi-modal fact-checking. In Proceedings of the ACM Web Conference 2024 (pp. 2429\u0026ndash;2440).\u003c/li\u003e\n \u003cli\u003eZhang, H. (2025). Fact-checking in China: Normative and strategic transparency of Chinese journalists in fact-checking reports. Asian Journal of Communication, 35(2), 81\u0026ndash;99.\u003c/li\u003e\n \u003cli\u003eZhang, J., Featherstone, J. D., Calabrese, C., \u0026amp; Wojcieszak, M. (2021). Effects of fact-checking social media vaccine misinformation on attitudes toward vaccines. Preventive Medicine, 145, 106408.\u003c/li\u003e\n \u003cli\u003eZhou, R. Z. [周润哲]. (2021). 孤独坚守: 后真相时代事实核查的逻辑困境 [A lonely stand: The logical dilemma of fact-checking in the post-truth era]. 中国报业 [China Press], (12), 20\u0026ndash;21.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"fact-checking cultures, misinformation governance, verification practices, authoritarian media systems, BERTopic","lastPublishedDoi":"10.21203/rs.3.rs-7421246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7421246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmid the global spread of misinformation, fact-checking has emerged as a key mechanism for ensuring informational accuracy. Yet existing research remains largely Western-centric, overlooking practices in authoritarian and hybrid media systems. Addressing this gap, this study examines the organisational characteristics, issue selection logics, and verification approaches of fact-checking entities in mainland China. Drawing on a mixed-methods design, it analysed 2,719 fact-checking articles from seven institutional actors and 3,026 video texts from three individual fact-checkers on Chinese TikTok (Douyin) between 2022 and 2025. The study integrated BERTopic modelling and manual content analysis, with 400 sampled cases assessed across verification dimensions such as claim accessibility, verdict style, evidentiary support, and citation diversity. Based on metadata, a fourfold typology was constructed: official, professional/commercial, independent/academic, and individual models. Findings show that Chinese fact-checking operates within a pluralistic yet state-integrated ecosystem shaped by political sensitivities, platform affordances, and audience expectations. Verification functions as a culturally embedded performance of epistemic authority. By conceptualising fact-checking as institutional boundary work, the study contributes to comparative debates on misinformation governance beyond liberal-democratic contexts.\u003c/p\u003e","manuscriptTitle":"Fact-Checking Organisations in China: Characteristics, Verification Approaches, and Practices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 01:01:58","doi":"10.21203/rs.3.rs-7421246/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":"063b7fee-5c82-4a9f-9279-6135e6030fea","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55526371,"name":"Humanities/Cultural and media studies"},{"id":55526372,"name":"Social science/Cultural and media studies"},{"id":55526373,"name":"Social science/Politics and international relations"}],"tags":[],"updatedAt":"2026-04-15T10:03:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 01:01:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7421246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7421246","identity":"rs-7421246","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}