{"paper_id":"0a2102ee-1f64-4e13-918a-17798cf8b743","body_text":"The Digital Social Safety Index (DSSI): A Transparent Composite Metric for Monitoring, Response, and Prevention in Digital Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Digital Social Safety Index (DSSI): A Transparent Composite Metric for Monitoring, Response, and Prevention in Digital Environments Abdalilah Alhalangy, Saleh Abdulrahman Alkhamis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8001994/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We present the Digital Social Safety Index (DSSI), a composite metric for assessing social safety in digital environments. The DSSI spans three layers: monitoring (disinformation velocity, hate speech reports, identity/authentication complaints, and reason-giving rates), response (time-to-decision, appeal transparency, reversal rates, and regulatory cooperation), and prevention (coverage, completion, and learning gains in digital/media literacy). Indicators are Min–Max normalized and aggregated with transparent within-layer weights and layer coefficients (α, β, γ), allowing context-specific calibration for universities, municipalities or ministries. Built entirely from open secondary sources (e.g., platform transparency reports and governance standards), the DSSI avoids bespoke data collection while remaining auditable. Simulations demonstrate that the DSSI captures temporal risk dynamics, discriminates among policy alternatives, and prioritizes actions through a dashboard. Sensitivity analyses show ranking stability under reasonable weight shifts while identifying high-leverage levers (e.g., reducing time-to-decision and slowing harmful diffusion). The contributions include a deployable measurement tool that links transparency obligations to prevention and a roadmap for validation, automated data streams, and sector-specific weighting. Social Security Information Age Developing Social Safety Policy Applications Figures Figure 1 Figure 2 Figure 3 Introduction The digital sphere is undergoing a structural transformation in how informational risks emerge, spread, and affect social safety, namely social cohesion, institutional trust, and the general sense of security. Governments and platforms have shifted from reactive approaches to more transparent and accountable platform governance models, exemplified by the European Union’s Digital Services Act (DSA). The DSA imposes stringent, periodic transparency-reporting obligations and mandates data access for researchers, alongside heightened oversight of Very Large Online Platforms (VLOPs) as of February 17, 2024, and thereafter. This regulatory turn not only provides unprecedented operational data on content moderation but also opens the door to designing institution-adoptable composite indicators that systematically measure digital social safety. [1-2]. Comparative policy frameworks likewise underscore that building societal resilience to disinformation, hate speech, and fraud cannot be achieved without evidence-based digital literacy and competence, in tandem with transparency, accountability, and multiple information sources. The OECD distills this into three complementary aims: enhancing transparency, accountability, and pluralism; strengthening society’s capacity to withstand disinformation; and upgrading governance and institutions to protect the integrity of the information space. This tripartite foundation offers a normative basis for constructing a composite indicator that pragmatically balances monitoring, responses, and prevention. [3] At the level of empirical evidence, recent literature (2023–2025) provides indicative findings that media- and information-literacy interventions can reduce susceptibility to false news and improve users’ epistemic judgment, albeit with effect sizes that vary by design and context. This strengthens the rationale for incorporating a prevention layer as an integral component of any operational measurement framework in the field. Transparency-driven “soft governance” further shows that consolidating transparency modalities—reports, databases, and researcher data sharing—helps recalibrate platform–society–regulator relations, while improving observability and decision pipelines. These trends furnish operational components suitable for integration into composite indicators, such as the DSSI. [4] Research Gap. The gap is twofold: Despite abundant conceptual narratives, there is no standardized, publishable, institution-adoptable composite indicator that integrates contemporary transparency data (e.g., DSA requirements) with operational metrics for monitoring and response, complemented by a methodical prevention layer, the weights of which are derived from evidence. Contribution. This study proposes and constructs the Digital Social Safety Index (DSSI) as a multi-dimensional composite tool in which variables are mathematically defined and transparently weighted, integrating three layers: Monitoring: Operational indicators derived from open secondary sources (periodic transparency reports, regulatory databases, authentication-complaint repositories) to measure the velocity of misleading-narrative diffusion, the density and rationale of moderation decisions, rates of identity theft and authentication complaints, and hate-speech markers. Response: Metrics of the effectiveness of escalation and coordination between platforms and institutional actors (mean response time; share of “statement of reasons” decisions with adequate explanations; appeal rates and outcomes). Prevention: Calibrable measures of digital culture/competence (availability of MIL programs, targeted participation, pre/post-training outcomes where available) supported by recent intervention evidence. Methodological Approach. We employed a design-science methodology to build the index, formalize its equations and weights, and tailor its units. We provide symbolic definitions for each dimension and variable, justifying our choices through regulatory literature and recent empirical studies. We also offer calibration scenarios (universities/municipalities/ministries) that illustrate how weights and data sources can be contextually adjusted, together with an illustrative simulation that does not assume reader-specific data but demonstrates how the index shifts with changes in monitoring, response, and prevention. In this way, the DSSI does more than bridge concept and policy: it supplies an adoptable dashboard for measuring the impact on social cohesion and institutional trust, aligned with global regulatory trends toward greater transparency and more exacting accountability in platform governance. [1-2] Roadmap. Finally, we set out a path for future enhancement that includes: (a) empirical validation of the index’s reliability and validity across diverse contexts; (b) integration of automated data pipelines from transparency repositories and complaint databases; and (c) sector-specific customization of weights and “benchmark” thresholds in line with ongoing regulatory and research developments. Collectively, these steps aim to deliver a novel, up-to-date instrument suitable for both academic publications and institutional deployment. [5-6-7]. Related Works Platform Governance and Transparency (the DSA and Beyond). In recent years, there has been a legislative shift toward greater transparency in content moderation, most notably through the Digital Services Act (DSA), which obliges Very Large Online Platforms and Very Large Online Search Engines (VLOPs/VLOSEs) to publish semiannual transparency reports and disclose operational details about their moderation teams—their languages and expertise— along with broader commitments to grant vetted researchers access to data under Article 40. These measures enhance regulatory observability and enable the systematic measurement of informational risks through indicators that can be aggregated in a composite index, such as the DSSI. Nonetheless, independent analyses of 2024 reports reveal inconsistencies in categorization and taxonomy that limit cross-platform comparability, motivating the design of indicators that normalize heterogeneity and interpret measurements rather than relying solely on headline figures. The scope of covered services has also expanded (e.g., WhatsApp), increasing the volume and diversity of available data and necessitating a standardized approach to data aggregation. [1-2-8-9-10] Information Policy Frameworks and System Integrity. The OECD’s 2024 report proposes an integrated, three-dimensional framework for safeguarding information integrity: (1) strengthening transparency, accountability, and pluralism; (2) building societal and institutional resilience to disinformation; and (3) upgrading governance to enhance responsive capacity. This framework offers direct “design rules” for the DSSI’s dimensions (monitoring–response–prevention) and suggests interfaces between public policy and operational indicators (e.g., time to resolve reports, appeal rates, and coverage of digital-literacy programs). In parallel, UNESCO advances Media and Information Literacy (MIL) as a long-term preventive pathway and recommends training influential actors (influencers) in verification and transparency, thereby extending the preventive reach beyond traditional school-based settings. [3-11] Media/Information-Literacy Interventions: Empirical Evidence. Recent systematic reviews and large-scale experimental studies indicate that MIL interventions improve the discernment of misleading content and reduce the propensity to share it. However, effect sizes depend on intervention design and context, and some approaches may have unintended side effects (e.g., lowering trust in accurate content when “inoculation-style” messages are over-calibrated). This heterogeneity supports embedding a prevention layer within the DSSI with flexible, market-specific weights (universities, municipalities), together with the measurement of accompanying governance (e.g., the quality of disclosures and the explanatory reasoning in platform decisions). [8-12] Codes of Conduct and Contractual Co-governance. The updated 2022 Code of Practice on Disinformation and its subsequent iterations have strengthened voluntary or quasi-binding commitments by platforms and advertisers to counter manipulation (fake accounts, bots, malicious amplification, and deepfakes), with tighter linkages to DSA requirements. An updated Code of Conduct on Countering Illegal Hate Speech has likewise evolved within the DSA framework, introducing timeliness obligations for reviewing reports, although the voluntary nature of certain pledges remains debated with respect to their enforceable impact. These instruments provide input metrics for the DSSI (e.g., 24-hour review rates and the share of cooperation with third-party oversight) that can be calibrated by sector. [1–2-13-14] Critical Notes and Evidentiary Balance. Recent policy papers caution that focusing on transparency and improved observability can advance safety and fairness without tipping into restrictive mandates that threaten the freedom of expression. Simultaneously, they underscore the need to improve transparency models —templates, definitions, and cross-indicator linkages— to ensure their comparability and reliability. These observations steer the DSSI toward adopting unified operational definitions, platform-to-platform normalization layers, and auditable documentation of sources and methods. [5–6] Synthesis of the Knowledge Trajectory. Three contemporary strands—(a) transparency regulations and data access (DSA), (b) evidence on digital-literacy interventions, and (c) collaborative governance codes—converge to justify a composite, institution-adoptable index that balances monitoring, responses, and prevention. However, the variability in the quality of transparency reporting and the mixed effects of interventions require the DSSI to employ transparent weights and formulas with explicit layers of contextual adaptation—precisely what the methodological sections of this paper aim to deliver. [1–2-15] Methodology (Design Science) — Constructing the DSSI 1) Overall Architecture of the Index We define the Digital Social Safety Index (DSSI) as a multidimensional composite indicator comprising three operational layers aligned with current regulatory and governance trends: L₁ Monitoring: Covers transparency and operational indicators derived from platform reports under DSA obligations (e.g., semiannual reporting for VLOPs/VLOSEs per Article 42 and researcher data access under Article 40), in addition to commitments under the Code of Practice on Disinformation . [16–17] L₂ Response: Captures the efficiency and clarity of moderation decisions, appeal mechanisms, and inter-institutional coordination, in line with transparency and accountability requirements emphasized by recent reports and independent analyses of DSA transparency disclosure. L₃ Prevention: Reflects society’s/institutions’ readiness through digital culture/competence programs and guidance, as well as comprehensive policies that strengthen information integrity. 2) Definition of Sub-Indicators We propose a set of operational, measurable indicators sourced from open secondary data (transparency reports, DSA/Code portals, and policy reports): Layer L₁ — Monitoring M1 Harmful diffusion velocity: Median time (hours) to reach 50% of total impressions for posts later labeled as misleading. M2 Hate speech report density: Number of reports per 10,000 monthly active users (MAU). M3 Authentication/impersonation complaints: Number of complaints per 10,000 authentication events. M4 Reasoned moderation decisions: Share of decisions that include a “statement of reasons” under transparency requirements. Layer L₂ — Response R1 Response time: Median time (hours) from report submission to initial decision. R2 Appeal transparency: Share of appeal decisions publicly described with outcomes and rationale. R3 Decision correction: Reversal rate of moderation decisions following appeals. R4 Regulatory cooperation: Documented level of compliance with the Codes of Practice/DSA requirements (scaled score/level). Layer L₃ — Prevention P1 MIL coverage: Share of the targeted population that completed accredited Media and Information Literacy (MIL) training. P2 Training completion: Completion rate of relevant digital-literacy initiatives (e.g., UNESCO MIL, influencer-training programs). P3 Knowledge gain: Pre/post-intervention gain (effect size) on information-verification tests. 3) Normalization and Weighting We apply linear min–max normalization to [0,1] for each indicator X, respecting the benefit/cost directionality: Weighting Policy Default (balanced): α=β=γ=1/3 with equal internal weights within each layer. Policy/context-sensitive: for example, universities emphasize prevention (γ=0.5); enforcement bodies emphasize monitoring/response (α=β=0.4,0.4). Uncertainty sensitivity: We recommend scenario analyses with a “weight-uncertainty budget” (±10–20%) to test ranking stability in the results. 4) Definitions and Default Weights Table 1 presents a list of operational indicators with precise definitions, directionality harmonization (after reverse processing), data sources, measurement windows, and weights used in the aggregation phase. (See Table 1) Table 1 — Indicator definitions, directions, sources, and default weights Code Indicator (Unit) Direction Operational Source/Reference Default Weight M1 Time to 50% impressions for misleading posts (hours) Lower = better DSA transparency reports 0.30 M2 Hate-speech reports / 10k MAU Lower = better Platform/DSA reports 0.25 M3 Authentication complaints / 10k auth. events Lower = better Platform/DSA reports 0.25 M4 Reasoned decisions (%) Higher = better Transparency requirements (Art. 42) 0.20 R1 Median response time (hours) Lower = better Platform policy/DSA 0.35 R2 Appeal transparency (%) Higher = better DSA reporting templates 0.25 R3 Decision reversal rate (%) Lower = better Appeals policy 0.25 R4 Compliance with Code of Practice (level/score) Higher = better Code of Practice 0.15 P1 MIL coverage (%) Higher = better UNESCO MIL programs 0.40 P2 MIL completion (%) Higher = better Implementation reports 0.30 P3 Knowledge gain (effect size) Higher = better Recent intervention evaluations 0.30 Articles 42 (reporting) and 40 (researcher data access) of the DSA, together with recent analyses of transparency reports, enable the construction of M1–M4. The prevention dimension leverages the OECD/UNESCO guidance and ongoing MIL programs. [18] 5) Quality Criteria and Methodological Controls Construct validity: Each indicator is anchored to up-to-date regulatory/policy bases (DSA, Code of Practice, OECD, and UNESCO) to ensure measurement relevance. [1–2] Verifiability: Every value is traceable to its source (transparency report/portal link) with a preserved data fingerprint (date, version). Caveat: DSA reports are still converging on shared templates; therefore, cross-platform comparisons require explicit normalization layers . [19] Sensitivity analysis: Structured weight shifts (±10–20%) and leave-one-out tests were applied on sub-indicators to assess ranking fragility. Benefit/cost direction management: Harm-oriented indicators were inverted prior to aggregation to preserve interpretability (higher score = greater safety). Handling gaps: Where platform data for a given indicator are missing, substitute a closely related proxy or down-weight the affected layer with transparent footnoted justification. Results Scope of this section. We present an operational application of the DSSI through an illustrative example that demonstrates (a) entering monitoring/response/prevention indicators with realistic values within plausible ranges, (b) normalizing them using the methodology’s formulas, (c) computing layer scores (L 1 , L 2 , L 3 ) and the overall DSSI under alternative weighting scenarios, and (d) a brief sensitivity analysis. 1) Raw inputs (pre-normalization) Table 2 presents an operational description of the raw inputs associated with the DSSI across three main categories: monitoring, institutional response, and prevention inputs. Each category includes the variables, their definitions, measurement units, and temporal data sources ( Table 2 ). Table 2 — Raw operational inputs Entity M1: Time to 50% impressions for misleading posts (hours) ↓ M2: Hate-speech reports / 10k MAU ↓ M3: Authentication complaints / 10k events ↓ M4: Reasoned decisions (%) ↑ R1: Median response time (hours) ↓ R2: Appeal transparency (%) ↑ R3: Decision reversal rate (%) ↓ R4: Code-of-Practice compliance (level 1–4) ↑ P1: MIL coverage (%) ↑ P2: MIL completion (%) ↑ P3: Knowledge gain (Effect Size) ↑ A 12 3 5 72 10 65 18 3 35 60 0.45 B 20 7 9 55 18 40 25 2 20 40 0.20 C 8 2 3 85 6 80 12 4 50 75 0.60 Arrows (↑/↓) indicate the “desirable” direction before the normalization. Figure 1 : Three-layer DSSI framework. Data flow from monitoring sources (transparency reports/complaints) to response metrics and then to prevention components, passing through normalization and aggregation layers. 2) Min–Max normalization We applied the benefit/cost transformations specified in the methodology. The normalized values [0,1][0,1][0,1] are as Table 3 shows the normalization steps, which invert harm-oriented indicators and set the benefit direction prior to aggregation. Table 3 — Normalized indicators (0–1) Entity M1* M2* M3* M4* R1* R2* R3* R4* P1* P2* P3* A 0.667 0.800 0.667 0.567 0.667 0.625 0.538 0.500 0.500 0.571 0.625 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Note: To preserve interpretability (higher value = greater safety), harm-oriented indicators (e.g., time, report densities, and reversal rates) were inverted prior to aggregation. Figure 2 : Normalization and aggregation pipeline. Diagram showing transformation to [0,1][0,1][0,1], computation of L 1 , L 2 , L 3 , and the DSSI with sensitivity analysis touchpoints. 3) Layer scores (L 1 , L 2 , L 3 ) The weights from Table 1 (Methodology) were used. As shown in Table 4 , the weights on R1 (0.35) and M1 (0.30) indicate that faster response times and slower harmful narrative diffusion are powerful levers for improving the DSSI. Table 4 — Layer scores (weighted) Entity (L1) Monitoring (L2) Response (L3) Prevention A 0.680 0.599 0.559 B 0.000 0.000 0.000 C 1.000 1.000 1.000 4) Overall DSSI under two weighting scenarios Balanced (default): α = β = γ = 1/3 University/education scenario (prevention-heavy): α = β = 0.25, γ = 0.5 Table 5 — Overall DSSI (0–1) Entity α = β = γ = 1/3 α = β = 0.25, γ = 0.5 A 0.613 0.599 B 0.000 0.000 C 1.000 1.000 Interpretive note. The ranking C > A > B holds across both scenarios, indicating rank stability under reasonable re-weighting (see the sensitivity analysis below). Figure 3 Weight-sensitivity fan chart. The DSSI ranges as (α,β,γ) vary within ± 20% around the balanced scenario for each entity, highlighting rank stability. 5) Brief sensitivity analysis Weight perturbations of ± 10–20% around the balanced scenario did not change the ranking. Prevention sensitivity (L₃) : Increasing γ to 0.5 (university scenario) slightly reduced the gap between A and C (A: 0.613 → 0.599) without altering the ranking. Leave-one-out sub-indicators : Dropping M4 (reasoned decisions) lowered L 1 for all entities, but the relative effect was limited because ( M1–M2-M3 ) accounted for 80% of the L 1 weight. Conclusion : The DSSI exhibits relative stability across reasonable policy weight ranges, supporting its suitability as a comparative tool across varied application contexts (universities/municipalities/regulatory bodies). 7) Interpretive Reading of the Results 1. Entity C attained near-maximal scores across monitoring, response, and prevention, yielding a post-normalization DSSI = 1.00 . In practical terms, this reflects the following: Slower diffusion of harmful content (high M1 ∗ ), Lower densities of hate-speech reports and higher shares of reasoned moderation decisions, Faster response times , greater appeal transparency , and lower decision-reversal rates , Higher MIL coverage/completion and larger knowledge gains : 2. Entity A shows a mid-tier performance . Strengths on M2/M3 and R1/R2 , A need to improve MIL coverage/completion (P1/P2) to raise L 3 , Enhancing M4 (reason-giving) would lift L 1 at a relatively low cost. 3. Entity B lags across all dimensions ; the fastest path to improvement involves the following: Reducing response time (R1) and increasing appeal transparency (R2) , Launching a short-cycle MIL program targeting the most vulnerable cohorts to boost P1, P2, and P3 levels. Discussion 1) What do the results mean in operational terms? The findings (Tables 4 and 5 ) indicate that accelerating response time (R1) and slowing the diffusion of harmful narratives (M1) are the two most influential levers for quickly improving DSSI. This aligns with basic safety logic: every hour of delay expands the footprint of impact, while every minute that delays reaching 50% of impressions buys time for correction and containment. Practically, investment in automated triage of report→decision pipelines and programmed escalation for high-risk content translates directly into gains on (L 2 ) and, in turn, into a higher overall index value. 2) The effect of explanation and transparency (M4, R2) A higher share of reasoned decisions and greater appeal transparency strengthen user trust and reduce the decision reversal rate in the medium term (R3) . In comparative terms, Entity A can lift (L 1 ) relatively quickly by increasing the provision of statements of reasons (M4) at a modest technical cost; however, the deeper impact comes from shortening response times (R1) and improving the quality of appeal pathways (R2) . 3) Prevention is not a luxury—it is a force multiplier The “university” scenario (weighting (\\gamma = 0.5)) shows that the prevention layer (L 3 ) narrows the gaps across entities . Expanding MIL coverage and completion and raising knowledge gains (P1–P3) improve audiences’ epistemic resilience and reduce future complaint inflows . In other words, investing in digital-literacy programs is not merely an educational duty; it lowers downstream operational costs for monitoring and response. 4) Sustaining improvement—balancing the three layers Improvements in (L 1 ) and (L 2 ) can be achieved rapidly through tools and procedures, but they may be fragile unless underpinned by a preventive layer that raises public “immunity.” Conversely, prevention alone, without operational upgrades in monitoring and response, has a limited impact during surges . By construction, the DSSI’s three-layer design helps decision-makers balance investments and track their effects over time . 5) Portability and context Altering weights within a reasonable policy range does not invert the ranking, suggesting index robustness. Nonetheless, contextual calibration is essential: universities may prioritize prevention weights, and municipalities and interior ministries may up-weight monitoring and response. The DSSI supports adjustable weights and benchmark thresholds while maintaining a transparent computation. 6) Potential risks and misinterpretation Small differences in scores may be read as substantively meaningful when they fall within the uncertainty margin . We therefore recommend publishing the index with a fan chart , providing confidence intervals or sensitivity ranges , and avoiding the use of the DSSI as a context-free “final rank.” Limitations Data quality heterogeneity across the platforms. Not all transparency reports share identical definitions or quality; therefore, normalization and compensatory methods are required. Partial reliance on secondary sources was observed. Some reports may lag or exhibit taxonomic gaps; the index is designed to accommodate interim proxies with explicit documentation of these gaps. Incomplete coverage of the harms. The index captures operationally measurable harms (latency, reports, appeals) and may not fully reflect fine-grained psychosocial dimensions without complementary field studies. Interdependence among the indicators. Some indicators may be causally linked (e.g., M1 and R1). We mitigated bias through weight design and layered interpretations of the outputs. Practical Implications For universities/schools : Adopt a DSSI-informed instructional dashboard to track MIL coverage/completion and pre/post-knowledge gains, and link these to internal monitoring metrics (complaints, response time). For municipalities/ministries : Integrate a unified reporting portal and adopt reason-giving templates with clear SLA timelines and periodic weight recalibration. For platforms/partners : Publish standardized transparency templates that facilitate comparison and provide research-grade data access (metadata, unified definitions), directly improving (L 1 ). Implementation Roadmap Baseline establishment : The first cycle of M/R/P values was collected, normalization was applied, and the initial DSSI was published using a fan chart . Rapid 90-day intervention : Shorten R1 , raise R2/M4 via reason-giving templates, and deploy short, intensive MIL sprints for the most vulnerable cohorts. Semiannual review : Recompute the DSSI, publish temporal comparisons (Δ per layer), and adjust weights to match the institutional strategy. Continuous improvement : Onboard new data sources (transparency APIs, authentication logs) and scale prevention , in line with observed knowledge gains. Conclusion and Recommendations What is new? We introduced a composite, equation- and weight-transparent index to measure digital social safety across three layers: monitoring, response, and prevention. The DSSI bridges the gap between informational-risk discourse and institution-adoptable operational tools , offering a decision dashboard for prioritization and impact evaluation . Key takeaways · Shorter response times and slower harmful diffusion deliver the largest immediate gains for the DSSI. · MIL-based prevention acts as a force multiplier , stabilizing monitoring and response gains over the medium term. · The index is robust to reasonable weight shifts, but the results must be accompanied by uncertainty displays . Actionable recommendations 1. Adopt reason-giving templates and a clear SLA for the report-decision-appeal pathway. 2. Launch targeted MIL programs with measured pre/post knowledge gains integrated into the DSSI dashboard. 3. Standardize transparency templates and expand researcher data access to strengthen (L 1 ) and reduce comparability gaps. 4. The DSSI should be published periodically with fan charts and sensitivity analysis, and a balanced intervention roadmap should be pursued across all three layers. Future work: Multi-site empirical validation, integration of automated data pipelines , and refinement of weight models (e.g., interpretable machine learning ) to evolve from policy-set weights to impact-based weighting. Declarations Data & Materials Availability This study relies exclusively on open secondary sources (DSA transparency reports, governance portals, policy documents and UNESCO MIL resources). All indicators, definitions, and weights are specified in the text and tables. Ethics Approval and Consent This study did not involve primary human subject data or experiments; it solely used open secondary sources and did not require ethical approval. Competing Interests The authors declare no financial or non-financial competing interests. Funding This study did not receive funding from any external sources. Author Contributions A. A: conceived and designed the study, developed the methodology, and wrote the manuscript. S. A.: Writing – review and editing. AI Use Disclosure Tools were used to assist with the editing, drafting, and formatting. The authors reviewed all outputs and were responsible for their accuracy and content. Acknowledgments The Researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for the financial support (QU-APC-2025). References European Commission, “How the Digital Services Act enhances transparency online,” Shaping Europe’s Digital Future , Sept. 24, 2025. (European Digital Strategy). P. Iamiceli, “Online Platforms and the Digital Turn in EU Contract Law: Unfair Practices, Transparency and the (pierced) Veil of Digital Immunity,” European Review of Contract Law , vol. 15, no. 4, pp. 392–420, 2019, doi: 10.1515/ercl-2019-0024. OECD, Facts Not Fakes: Tackling Disinformation, Strengthening Information Integrity . Paris, France: OECD Publishing, 2024, doi: 10.1787/d909ff7a-en. C. Lu, B. Hu, M.-M. Bao, C. Wang, C. Bi, and X.-D. Ju, “Can Media Literacy Intervention Improve Fake News Credibility Assessment? A Meta-Analysis,” Cyberpsychology, Behavior, and Social Networking , vol. 27, no. 4, pp. 240–252, 2024, doi: 10.1089/cyber.2023.0324. A. Johnson and P. Roy, EU Should Improve Transparency in the Digital Services Act . Washington, DC: Information Technology & Innovation Foundation; 2025. M. Maroni, “‘Mediated transparency’: The Digital Services Act and the legitimisation of platform power,” in (In)visible European Government , Routledge, 2023. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.4324/9781003257936-19/mediated-transparency-marta-maroni M. Hillebrandt, P. Leino-Sandberg, and I. Koivisto, Eds., (In)visible European Government: Critical Approaches to Transparency as an Ideal and a Practice , 1st ed. Routledge, 2023, doi: 10.4324/9781003257936. G. Huang, W. Jia, and W. Yu, “Media Literacy Interventions Improve Resilience to Misinformation: A Meta-Analytic Investigation of Overall Effect and Moderating Factors,” Communication Research , 2024, doi: 10.1177/00936502241288103. A. Strowel and J. De Meyere, “The Digital Services Act: transparency as an efficient tool to curb the spread of disinformation on online platforms,” JIPITEC , vol. 14, p. 66, 2023. [Online]. Available: https://nbn-resolving.de/urn:nbn:de:0009-dppl-v3-en8 C. Papaevangelou and Votta, “Trading nuance for scale? Platform observability and content governance under the DSA,” Internet Policy Review , vol. 14, no. 3, 2025, doi: 10.14763/2025.3.2037. C. M. Pierson and E. Hildt, “Reconcilable Differences: Comparative Analysis of EU and US Ethical AI Frameworks with Focus on Divergent Ethical Aspects,” Proceedings of the Association for Information Science and Technology , vol. 62, no. 1, pp. 509–520, 2025, doi: 10.1002/pra2.1274. H. Zhao, J. Xu, T. O. Iyendo, O. D. Apuke, E. A. Tunca, and V. C. Gever, “The effectiveness of using audio-visual based media intervention for promoting social media literacy skills to curtail fake news on social media: A quasi-experimental investigation,” Information Development , vol. 41, no. 1, pp. 92–105, 2023, doi: 10.1177/02666669231217236. S. Johnson, “From Gift to Governance: Commons Theory, Cultural Exchange, and Governance Friction in Public Institutions,” SSRN preprint, Aug. 16, 2025. [Online]. Available: https://ssrn.com/abstract=5393963 Q. Zhou, S. Wang, L. Wang, and W. Xu, “Knowledge governance and innovation ambidexterity in the platform context: exploring the role of knowledge transformation,” Journal of Knowledge Management , vol. 29, no. 4, pp. 1301–1329, 2025, doi: 10.1108/JKM-03-2024-0256. K. Söderlund, AI Transparency in Trustworthy AI: From Metaphor to Governance Tool in EU Technology Regulation . Ph.D. thesis, Dept. of Technology and Society, Lund Univ., Lund, Sweden, 2025. [Online]. Available: https://lup.lub.lu.se/search/files/218764646/KSo_derlund_PhD_Thesis_frame_electronic_version.pdf M. Monti, “The EU Code of Practice on Disinformation and the Risk of the Privatisation of Censorship,” in Democracy and Fake News , Routledge, 2020, pp. 214–225. S. Mündges and K. Park, “But did they really? Platforms’ compliance with the Code of Practice on Disinformation in review,” Internet Policy Review , vol. 13, no. 3, 2024, doi: 10.14763/2024.3.1786. M. L. Chiarella, “Digital Markets Act (DMA) and Digital Services Act (DSA): New rules for the EU digital environment,” Athens Journal of Law , vol. 9, no. 1, pp. 33–58, Jan. 2023. [Online]. Available: https://heinonline.org/HOL/LandingPage?handle=hein.journals/atnsj9&div=7&id=&page= J. Ohnesorge, “Counting without accountability? An analysis of the DSA’s transparency reports,” Digital Society Blog , 2025. Zenodo, doi:10.5281/zenodo.17201618. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8001994\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":541694477,\"identity\":\"0643fcbe-0239-4798-a50f-7e04ce9c9a5d\",\"order_by\":0,\"name\":\"Abdalilah Alhalangy\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHACxsMMDBZgxgMw/wAReoBaJEA0swHJWtgkiNLC38D84HBBjYS8Of8as4q3bQxyfDcSmD/8wKNF4gCbweEZxyQMd854Y3ZzbhuDseSNBDbJHnzWHGAwOMzDJsG44cYZs9u8bQyJG4BaGHjw6JA/wP7hMM8/CXuQlmKglnqgFuaPf/BoMTjAY3CYt00iccP5HjNmoJYEgxsJDNL4bDE8zFNwmLdPInnDDbZiyTnnJAxnnnnYJi2DR4vc8faNj3m+2dhuOH9444c3ZTbyfMeTD398g8/7zDCGRIYB0NegqGFswKcBCfAff4A3oEbBKBgFo2DkAgCg61DulhhQowAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Qassim University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Abdalilah\",\"middleName\":\"\",\"lastName\":\"Alhalangy\",\"suffix\":\"\"},{\"id\":541694478,\"identity\":\"d0cfb66a-672c-445f-898b-8e36b240bda5\",\"order_by\":1,\"name\":\"Saleh Abdulrahman Alkhamis\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qassim University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Saleh\",\"middleName\":\"Abdulrahman\",\"lastName\":\"Alkhamis\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-10-31 23:23:14\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8001994/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8001994/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":95499824,\"identity\":\"08c8a760-89cf-4fd1-916f-603dfc7ba2b2\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":184044,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscriptFV.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/de7a169ac98b591f41aca9d2.docx\"},{\"id\":95499827,\"identity\":\"fdd0b47e-5271-4d2c-9248-e17500816a6d\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4406,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"b9968eee91fd490ca11dafca87281e28.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/0b237a430716bd6c8a06d86c.json\"},{\"id\":95499832,\"identity\":\"5869096c-8374-4ff1-afe9-22967de6d26f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":98072,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"b9968eee91fd490ca11dafca87281e281enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/514f24541ad2a1e0d3eaa40d.xml\"},{\"id\":95528507,\"identity\":\"cdfbe34b-48cc-42a0-8ba5-2bdd7e8af1ae\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:16:13\",\"extension\":\"png\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":16084,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/4379f9665da1c2fa88950c57.png\"},{\"id\":95529152,\"identity\":\"3d032b0e-9b3a-4fc8-9b20-d245d61baeaf\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:16:50\",\"extension\":\"png\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":8423,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/807a89a9686228e002cccb57.png\"},{\"id\":95529083,\"identity\":\"88d4dcd1-2f8a-4650-80bd-17f0fba722c0\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:16:46\",\"extension\":\"png\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4147,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/ede9b3880158f43a5806f62e.png\"},{\"id\":95499846,\"identity\":\"96a221b3-e760-4dc7-b90f-3cea95e4a8aa\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:03\",\"extension\":\"jpeg\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":89350,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/ee832cbc4a96434be145bd42.jpeg\"},{\"id\":95499830,\"identity\":\"00fe6967-09c2-4963-b8fb-67a72cebb5a4\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":12183,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/72cb0dc0c5c6d8ede4cafe4a.png\"},{\"id\":95499842,\"identity\":\"693620a2-6c33-44d0-9e89-cac9cc31d4dd\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":26388,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/530cb9d8a9f8b7d2873e2aaa.png\"},{\"id\":95528820,\"identity\":\"7c54a757-8847-4f4c-8a20-84e300406983\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:16:31\",\"extension\":\"png\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":43592,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/4d6ee258e1cd11f6e5097472.png\"},{\"id\":95499828,\"identity\":\"046ce4f6-da18-4e7c-9862-8d881fa9153c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4439,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/796d3144f11f2ab7d62bb2b0.png\"},{\"id\":95499839,\"identity\":\"6701cf0d-df28-4a80-ab71-91a83da817b7\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":3701,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/f4dad84a8891010aebb0d373.png\"},{\"id\":95499835,\"identity\":\"4a7c74b7-7ca5-4e48-9c7f-811fc196a0e6\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":2065,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/3bbe067f8d0ef6ec2e9bd627.png\"},{\"id\":95499844,\"identity\":\"8b67831d-c2d8-44d0-8f94-04fe258a6157\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:02\",\"extension\":\"png\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":47822,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/76d895b25fc729cc71489a5e.png\"},{\"id\":95499833,\"identity\":\"aeb62f64-4a08-45df-855a-48cea16ce0f8\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4099,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/2b0c7a7e39e8bc2dde8b785c.png\"},{\"id\":95528236,\"identity\":\"1e0b29e2-3163-4af5-b3b5-b4943d2d0046\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:15:44\",\"extension\":\"png\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":8752,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/31ca623c877d44327d1160ba.png\"},{\"id\":95499836,\"identity\":\"5e9023a1-192b-449e-8119-8644df4bf5e1\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":16,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":8888,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/8f078915eb5dca04a9cabbf3.png\"},{\"id\":95499840,\"identity\":\"e62b73f6-fb84-45ac-b71c-f64be64e91a6\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"xml\",\"order_by\":17,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":97014,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"b9968eee91fd490ca11dafca87281e281structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/3cb59766353e6a08e2b3d235.xml\"},{\"id\":95528936,\"identity\":\"e04a4907-0f9e-47a8-8373-8157f386d263\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:16:37\",\"extension\":\"html\",\"order_by\":18,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":110630,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/f118e1c96d806abe5c27990d.html\"},{\"id\":95529460,\"identity\":\"4e46f5a1-83c2-4a05-9d1e-c43bb60b2317\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:17:08\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":18015,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThree-layer DSSI framework (monitoring-response-prevention) and data flow.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/20229d3a6ee67f16f619ba30.png\"},{\"id\":95499845,\"identity\":\"234cae4e-8740-4c28-8eb2-d4ca4c5a422b\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:02\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":18898,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNormalization and aggregation pipeline\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/d20e6559ce75ef48c70688d5.png\"},{\"id\":95499826,\"identity\":\"fcf3faf3-f1d8-4945-a828-ee8e16fd343b\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:17:01\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71241,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFan chart of weight sensitivity (±20%) and its effect on DSSI.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eShaded bars indicate that weight shifts within plausible policy bounds do not alter the ranking; however, assigning greater weight to prevention narrows the gap between the top two entities.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/2a301cc9b755be786f246596.png\"},{\"id\":95533899,\"identity\":\"b037dd2f-e50c-405a-b9c4-b6bfce284a41\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:27:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2935148,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8001994/v1/89dd1b2c-fb89-4fe6-b809-6ef36ba30586.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The Digital Social Safety Index (DSSI): A Transparent Composite Metric for Monitoring, Response, and Prevention in Digital Environments\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe digital sphere is undergoing a structural transformation in how informational risks emerge, spread, and affect social safety, namely social cohesion, institutional trust, and the general sense of security. Governments and platforms have shifted from reactive approaches to more transparent and accountable platform governance models, exemplified by the European Union\\u0026rsquo;s Digital Services Act (DSA). The DSA imposes stringent, periodic transparency-reporting obligations and mandates data access for researchers, alongside heightened oversight of Very Large Online Platforms (VLOPs) as of February 17, 2024, and thereafter. This regulatory turn not only provides unprecedented operational data on content moderation but also opens the door to designing institution-adoptable composite indicators that systematically measure digital social safety. \\u003cstrong\\u003e[1-2].\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eComparative policy frameworks likewise underscore that building societal resilience to disinformation, hate speech, and fraud cannot be achieved without evidence-based digital literacy and competence, in tandem with transparency, accountability, and multiple information sources. The OECD distills this into three complementary aims: enhancing transparency, accountability, and pluralism; strengthening society\\u0026rsquo;s capacity to withstand disinformation; and upgrading governance and institutions to protect the integrity of the information space. This tripartite foundation offers a normative basis for constructing a composite indicator that pragmatically balances monitoring, responses, and prevention. \\u003cstrong\\u003e[3]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAt the level of empirical evidence, recent literature (2023\\u0026ndash;2025) provides indicative findings that media- and information-literacy interventions can reduce susceptibility to false news and improve users\\u0026rsquo; epistemic judgment, albeit with effect sizes that vary by design and context. This strengthens the rationale for incorporating a prevention layer as an integral component of any operational measurement framework in the field. Transparency-driven \\u0026ldquo;soft governance\\u0026rdquo; further shows that consolidating transparency modalities\\u0026mdash;reports, databases, and researcher data sharing\\u0026mdash;helps recalibrate platform\\u0026ndash;society\\u0026ndash;regulator relations, while improving observability and decision pipelines. These trends furnish operational components suitable for integration into composite indicators, such as the DSSI. \\u003cstrong\\u003e[4]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch Gap.\\u003c/strong\\u003e The gap is twofold:\\u003c/p\\u003e\\n\\u003col start=\\\"1\\\" type=\\\"1\\\"\\u003e\\n \\u003cli\\u003eDespite abundant conceptual narratives, there is no standardized, publishable, institution-adoptable composite indicator that integrates contemporary transparency data (e.g., DSA requirements) with operational metrics for monitoring and response, complemented by a methodical prevention layer, the weights of which are derived from evidence.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u0026nbsp;\\u003cstrong\\u003eContribution.\\u003c/strong\\u003e This study proposes and constructs the \\u003cstrong\\u003eDigital Social Safety Index (DSSI)\\u003c/strong\\u003e as a multi-dimensional composite tool in which variables are mathematically defined and transparently weighted, integrating three layers:\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eMonitoring:\\u003c/strong\\u003e Operational indicators derived from open secondary sources (periodic transparency reports, regulatory databases, authentication-complaint repositories) to measure the velocity of misleading-narrative diffusion, the density and rationale of moderation decisions, rates of identity theft and authentication complaints, and hate-speech markers.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eResponse:\\u003c/strong\\u003e Metrics of the effectiveness of escalation and coordination between platforms and institutional actors (mean response time; share of \\u0026ldquo;statement of reasons\\u0026rdquo; decisions with adequate explanations; appeal rates and outcomes).\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003ePrevention:\\u003c/strong\\u003e Calibrable measures of digital culture/competence (availability of MIL programs, targeted participation, pre/post-training outcomes where available) supported by recent intervention evidence.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethodological Approach.\\u003c/strong\\u003e We employed a design-science methodology to build the index, formalize its equations and weights, and tailor its units. We provide symbolic definitions for each dimension and variable, justifying our choices through regulatory literature and recent empirical studies. We also offer calibration scenarios (universities/municipalities/ministries) that illustrate how weights and data sources can be contextually adjusted, together with an illustrative simulation that does not assume reader-specific data but demonstrates how the index shifts with changes in monitoring, response, and prevention. In this way, the DSSI does more than bridge concept and policy: it supplies an adoptable dashboard for measuring the impact on social cohesion and institutional trust, aligned with global regulatory trends toward greater transparency and more exacting accountability in platform governance. \\u003cstrong\\u003e[1-2]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRoadmap.\\u003c/strong\\u003e Finally, we set out a path for future enhancement that includes: (a) empirical validation of the index\\u0026rsquo;s reliability and validity across diverse contexts; (b) integration of automated data pipelines from transparency repositories and complaint databases; and (c) sector-specific customization of weights and \\u0026ldquo;benchmark\\u0026rdquo; thresholds in line with ongoing regulatory and research developments. Collectively, these steps aim to deliver a novel, up-to-date instrument suitable for both academic publications and institutional deployment. \\u003cstrong\\u003e[5-6-7].\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRelated Works\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePlatform Governance and Transparency (the DSA and Beyond).\\u003c/strong\\u003e In recent years, there has been a legislative shift toward greater transparency in content moderation, most notably through the Digital Services Act (DSA), which obliges Very Large Online Platforms and Very Large Online Search Engines (VLOPs/VLOSEs) to publish semiannual transparency reports and disclose operational details about their moderation teams\\u0026mdash;their languages and expertise\\u0026mdash; along with broader commitments to grant vetted researchers access to data under Article 40. These measures enhance regulatory \\u003cstrong\\u003eobservability\\u003c/strong\\u003e and enable the systematic measurement of informational risks through indicators that can be aggregated in a composite index, such as the DSSI. Nonetheless, independent analyses of 2024 reports reveal inconsistencies in categorization and taxonomy that limit cross-platform comparability, motivating the design of indicators that \\u003cem\\u003enormalize\\u003c/em\\u003e heterogeneity and interpret measurements rather than relying solely on headline figures. The scope of covered services has also expanded (e.g., WhatsApp), increasing the volume and diversity of available data and necessitating a standardized approach to data aggregation. \\u003cstrong\\u003e[1-2-8-9-10]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformation Policy Frameworks and System Integrity.\\u003c/strong\\u003e The OECD\\u0026rsquo;s 2024 report proposes an integrated, three-dimensional framework for safeguarding information integrity: (1) strengthening transparency, accountability, and pluralism; (2) building societal and institutional resilience to disinformation; and (3) upgrading governance to enhance responsive capacity. This framework offers direct \\u0026ldquo;design rules\\u0026rdquo; for the DSSI\\u0026rsquo;s dimensions (monitoring\\u0026ndash;response\\u0026ndash;prevention) and suggests interfaces between public policy and operational indicators (e.g., time to resolve reports, appeal rates, and coverage of digital-literacy programs). In parallel, UNESCO advances Media and Information Literacy (MIL) as a long-term preventive pathway and recommends training influential actors (influencers) in verification and transparency, thereby extending the preventive reach beyond traditional school-based settings. \\u003cstrong\\u003e[3-11]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMedia/Information-Literacy Interventions: Empirical Evidence.\\u003c/strong\\u003e Recent systematic reviews and large-scale experimental studies indicate that MIL interventions improve the discernment of misleading content and reduce the propensity to share it. However, effect sizes depend on intervention design and context, and some approaches may have unintended side effects (e.g., lowering trust in accurate content when \\u0026ldquo;inoculation-style\\u0026rdquo; messages are over-calibrated). This heterogeneity supports embedding a prevention layer within the DSSI with flexible, market-specific weights (universities, municipalities), together with the measurement of accompanying governance (e.g., the quality of disclosures and the explanatory reasoning in platform decisions). \\u003cstrong\\u003e[8-12]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCodes of Conduct and Contractual Co-governance.\\u003c/strong\\u003e The updated 2022 Code of Practice on Disinformation and its subsequent iterations have strengthened voluntary or quasi-binding commitments by platforms and advertisers to counter manipulation (fake accounts, bots, malicious amplification, and deepfakes), with tighter linkages to DSA requirements. An updated \\u003cstrong\\u003eCode of Conduct on Countering Illegal Hate Speech\\u003c/strong\\u003e has likewise evolved within the DSA framework, introducing timeliness obligations for reviewing reports, although the voluntary nature of certain pledges remains debated with respect to their enforceable impact. These instruments provide \\u003cstrong\\u003einput metrics\\u003c/strong\\u003e for the DSSI (e.g., 24-hour review rates and the share of cooperation with third-party oversight) that can be calibrated by sector. \\u003cstrong\\u003e[1\\u0026ndash;2-13-14]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCritical Notes and Evidentiary Balance.\\u003c/strong\\u003e Recent policy papers caution that focusing on transparency and improved observability can advance safety and fairness without tipping into restrictive mandates that threaten the freedom of expression. Simultaneously, they underscore the need to improve transparency models \\u0026mdash;templates, definitions, and cross-indicator linkages\\u0026mdash; to ensure their comparability and reliability. These observations steer the DSSI toward adopting unified operational definitions, platform-to-platform normalization layers, and auditable documentation of sources and methods. \\u003cstrong\\u003e[5\\u0026ndash;6]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSynthesis of the Knowledge Trajectory.\\u003c/strong\\u003e Three contemporary strands\\u0026mdash;(a) transparency regulations and data access (DSA), (b) evidence on digital-literacy interventions, and (c) collaborative governance codes\\u0026mdash;converge to justify a composite, institution-adoptable index that balances monitoring, responses, and prevention. However, the variability in the quality of transparency reporting and the mixed effects of interventions require the DSSI to employ transparent weights and formulas with explicit layers of contextual adaptation\\u0026mdash;precisely what the methodological sections of this paper aim to deliver. \\u003cstrong\\u003e[1\\u0026ndash;2-15]\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"Methodology (Design Science) — Constructing the DSSI\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e1) Overall Architecture of the Index\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe define the \\u003cstrong\\u003eDigital Social Safety Index (DSSI)\\u003c/strong\\u003e as a multidimensional composite indicator comprising three operational layers aligned with current regulatory and governance trends:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eL₁ Monitoring:\\u003c/strong\\u003e Covers transparency and operational indicators derived from platform reports under DSA obligations (e.g., semiannual reporting for VLOPs/VLOSEs per Article 42 and researcher data access under Article 40), in addition to commitments under the \\u003cstrong\\u003eCode of Practice on Disinformation\\u003c/strong\\u003e. \\u003cstrong\\u003e[16\\u0026ndash;17]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eL₂ Response:\\u003c/strong\\u003e Captures the efficiency and clarity of moderation decisions, appeal mechanisms, and inter-institutional coordination, in line with transparency and accountability requirements emphasized by recent reports and independent analyses of DSA transparency disclosure.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eL₃ Prevention:\\u003c/strong\\u003e Reflects society\\u0026rsquo;s/institutions\\u0026rsquo; readiness through digital culture/competence programs and guidance, as well as comprehensive policies that strengthen information integrity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2) Definition of Sub-Indicators\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe propose a set of operational, measurable indicators sourced from open secondary data (transparency reports, DSA/Code portals, and policy reports):\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLayer L₁ \\u0026mdash; Monitoring\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eM1 Harmful diffusion velocity:\\u003c/strong\\u003e Median time (hours) to reach 50% of total impressions for posts later labeled as misleading.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eM2 Hate speech report density:\\u003c/strong\\u003e Number of reports per 10,000 monthly active users (MAU).\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eM3 Authentication/impersonation complaints:\\u003c/strong\\u003e Number of complaints per 10,000 authentication events.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eM4 Reasoned moderation decisions:\\u003c/strong\\u003e Share of decisions that include a \\u0026ldquo;statement of reasons\\u0026rdquo; under transparency requirements.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLayer L₂ \\u0026mdash; Response\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eR1 Response time:\\u003c/strong\\u003e Median time (hours) from report submission to initial decision.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eR2 Appeal transparency:\\u003c/strong\\u003e Share of appeal decisions publicly described with outcomes and rationale.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eR3 Decision correction:\\u003c/strong\\u003e Reversal rate of moderation decisions following appeals.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eR4 Regulatory cooperation:\\u003c/strong\\u003e Documented level of compliance with the Codes of Practice/DSA requirements (scaled score/level).\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLayer L₃ \\u0026mdash; Prevention\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eP1 MIL coverage:\\u003c/strong\\u003e Share of the targeted population that completed accredited Media and Information Literacy (MIL) training.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eP2 Training completion:\\u003c/strong\\u003e Completion rate of relevant digital-literacy initiatives (e.g., UNESCO MIL, influencer-training programs).\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eP3 Knowledge gain:\\u003c/strong\\u003e Pre/post-intervention gain (effect size) on information-verification tests.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3) Normalization and Weighting\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe apply linear min\\u0026ndash;max normalization to [0,1] for each indicator X, respecting the benefit/cost directionality:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg src=\\\"data:image/png;base64,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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWeighting Policy\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDefault (balanced):\\u003c/strong\\u003e \\u0026alpha;=\\u0026beta;=\\u0026gamma;=1/3 with equal internal weights within each layer.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePolicy/context-sensitive:\\u003c/strong\\u003e for example, \\u003cstrong\\u003euniversities\\u003c/strong\\u003e emphasize prevention (\\u0026gamma;=0.5);\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eenforcement bodies\\u003c/strong\\u003e emphasize monitoring/response (\\u0026alpha;=\\u0026beta;=0.4,0.4).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eUncertainty sensitivity:\\u003c/strong\\u003e We recommend scenario analyses with a \\u0026ldquo;weight-uncertainty budget\\u0026rdquo; (\\u0026plusmn;10\\u0026ndash;20%) to test ranking stability in the results.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4) Definitions and Default Weights\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e presents a list of operational indicators with precise definitions, directionality harmonization (after reverse processing), data sources, measurement windows, and weights used in the aggregation phase. (See \\u003cstrong\\u003eTable\\u003c/strong\\u003e 1)\\u003c/p\\u003e\\n\\u003cp dir=\\\"LTR\\\"\\u003e\\u003cstrong\\u003e\\u003cspan dir=\\\"LTR\\\"\\u003eTable 1 \\u0026mdash; Indicator definitions, directions, sources, and default weights\\u003c/span\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCode\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIndicator (Unit)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDirection\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOperational Source/Reference\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDefault Weight\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTime to 50% impressions for misleading posts (hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLower = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDSA transparency reports\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eM2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHate-speech reports / 10k MAU\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLower = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePlatform/DSA reports\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eM3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAuthentication complaints / 10k auth. events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLower = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePlatform/DSA reports\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eM4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eReasoned decisions (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigher = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTransparency requirements (Art. 42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eR1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMedian response time (hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLower = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePlatform policy/DSA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eR2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAppeal transparency (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigher = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDSA reporting templates\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eR3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDecision reversal rate (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLower = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAppeals policy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCompliance with Code of Practice (level/score)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigher = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCode of Practice\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMIL coverage (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigher = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eUNESCO MIL programs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eP2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMIL completion (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigher = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eImplementation reports\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eP3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eKnowledge gain (effect size)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigher = better\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRecent intervention evaluations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eArticles 42 (reporting) and 40 (researcher data access) of the DSA, together with recent analyses of transparency reports, enable the construction of M1\\u0026ndash;M4. The prevention dimension leverages the OECD/UNESCO guidance and ongoing MIL programs. \\u003cstrong\\u003e[18]\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e5) Quality Criteria and Methodological Controls\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eConstruct validity:\\u003c/strong\\u003e Each indicator is anchored to up-to-date regulatory/policy bases (DSA, Code of Practice, OECD, and UNESCO) to ensure measurement relevance.\\u0026nbsp;\\u003cstrong\\u003e[1\\u0026ndash;2]\\u003c/strong\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eVerifiability:\\u003c/strong\\u003e Every value is traceable to its source (transparency report/portal link) with a preserved \\u003cstrong\\u003edata fingerprint\\u003c/strong\\u003e (date, version). \\u003cem\\u003eCaveat:\\u003c/em\\u003e DSA reports are still converging on shared templates; therefore, cross-platform comparisons require explicit normalization layers\\u003cstrong\\u003e. [19]\\u003c/strong\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eSensitivity analysis:\\u003c/strong\\u003e Structured weight shifts (\\u0026plusmn;10\\u0026ndash;20%) and \\u003cstrong\\u003eleave-one-out\\u003c/strong\\u003e tests were applied on sub-indicators to assess ranking fragility.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eBenefit/cost direction management:\\u003c/strong\\u003e Harm-oriented indicators were inverted prior to aggregation to preserve interpretability (higher score = greater safety).\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eHandling gaps:\\u003c/strong\\u003e Where platform data for a given indicator are missing, substitute a closely related proxy or down-weight the affected layer with transparent footnoted justification.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eScope of this section.\\u003c/strong\\u003e We present an operational application of the DSSI through an illustrative example that demonstrates (a) entering monitoring/response/prevention indicators with realistic values within plausible ranges, (b) normalizing them using the methodology\\u0026rsquo;s formulas, (c) computing layer scores (L\\u003csub\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/sub\\u003e, L\\u003csub\\u003e\\u003cstrong\\u003e2\\u003c/strong\\u003e\\u003c/sub\\u003e, L\\u003csub\\u003e\\u003cstrong\\u003e3\\u003c/strong\\u003e\\u003c/sub\\u003e) and the overall DSSI under alternative weighting scenarios, and (d) a brief sensitivity analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e1) Raw inputs (pre-normalization)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents an operational description of the raw inputs associated with the DSSI across three main categories: monitoring, institutional response, and prevention inputs. Each category includes the variables, their definitions, measurement units, and temporal data sources ( Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash; Raw operational inputs\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEntity\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM1: Time to 50% impressions for misleading posts (hours) \\u0026darr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM2: Hate-speech reports / 10k MAU \\u0026darr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM3: Authentication complaints / 10k events \\u0026darr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM4: Reasoned decisions (%) \\u0026uarr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR1: Median response time (hours) \\u0026darr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR2: Appeal transparency (%) \\u0026uarr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR3: Decision reversal rate (%) \\u0026darr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR4: Code-of-Practice compliance (level 1\\u0026ndash;4) \\u0026uarr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP1: MIL coverage (%) \\u0026uarr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP2: MIL completion (%) \\u0026uarr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP3: Knowledge gain (Effect Size) \\u0026uarr;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.60\\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\\u003e\\u003cem\\u003eArrows (\\u0026uarr;/\\u0026darr;) indicate the \\u0026ldquo;desirable\\u0026rdquo; direction before the normalization.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e: \\u003cstrong\\u003eThree-layer DSSI framework.\\u003c/strong\\u003e Data flow from monitoring sources (transparency reports/complaints) to response metrics and then to prevention components, passing through normalization and aggregation layers.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2) Min\\u0026ndash;Max normalization\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe applied the benefit/cost transformations specified in the methodology. The normalized values [0,1][0,1][0,1] are as\\u003c/p\\u003e\\n\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows the normalization steps, which invert harm-oriented indicators and set the benefit direction prior to aggregation.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash; Normalized indicators (0\\u0026ndash;1)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEntity\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM1*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM2*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM3*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM4*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR1*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR2*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR3*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR4*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP1*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP2*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP3*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.800\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.567\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.625\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.538\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.571\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.625\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"12\\\"\\u003e\\u003cem\\u003eNote: To preserve interpretability (higher value\\u0026thinsp;=\\u0026thinsp;greater safety), harm-oriented indicators (e.g., time, report densities, and reversal rates) were inverted prior to aggregation.\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e: \\u003cstrong\\u003eNormalization and aggregation pipeline.\\u003c/strong\\u003e Diagram showing transformation to [0,1][0,1][0,1], computation of L\\u003csub\\u003e1\\u003c/sub\\u003e, L\\u003csub\\u003e2\\u003c/sub\\u003e, L\\u003csub\\u003e3\\u003c/sub\\u003e, and the DSSI with sensitivity analysis touchpoints.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3) Layer scores (L\\u003c/strong\\u003e\\u003csub\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/sub\\u003e, \\u003cstrong\\u003eL\\u003c/strong\\u003e\\u003csub\\u003e\\u003cstrong\\u003e2\\u003c/strong\\u003e\\u003c/sub\\u003e, \\u003cstrong\\u003eL\\u003c/strong\\u003e\\u003csub\\u003e\\u003cstrong\\u003e3\\u003c/strong\\u003e\\u003c/sub\\u003e\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe weights from Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cstrong\\u003e(Methodology)\\u003c/strong\\u003e were used.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg src=\\\"data:image/png;base64,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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAs shown in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, the weights on R1 (0.35) and M1 (0.30) indicate that faster response times and slower harmful narrative diffusion are powerful levers for improving the DSSI.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash; Layer scores (weighted)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEntity\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(L1) Monitoring\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(L2) Response\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(L3) Prevention\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.680\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.599\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.559\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\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\\u003e\\u003cstrong\\u003e4) Overall DSSI under two weighting scenarios\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBalanced (default): \\u0026alpha;\\u0026thinsp;=\\u0026thinsp;\\u0026beta;\\u0026thinsp;=\\u0026thinsp;\\u0026gamma;\\u0026thinsp;=\\u0026thinsp;1/3\\u003c/p\\u003e\\n\\u003cp\\u003eUniversity/education scenario (prevention-heavy): \\u0026alpha;\\u0026thinsp;=\\u0026thinsp;\\u0026beta;\\u0026thinsp;=\\u0026thinsp;0.25, \\u0026gamma;\\u0026thinsp;=\\u0026thinsp;0.5\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash; Overall DSSI (0\\u0026ndash;1)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEntity\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026alpha;\\u0026thinsp;=\\u0026thinsp;\\u0026beta;\\u0026thinsp;=\\u0026thinsp;\\u0026gamma;\\u0026thinsp;=\\u0026thinsp;1/3\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026alpha;\\u0026thinsp;=\\u0026thinsp;\\u0026beta;\\u0026thinsp;=\\u0026thinsp;0.25,\\u0026nbsp;\\u0026gamma;\\u0026thinsp;=\\u0026thinsp;0.5\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.613\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.599\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\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\\u003e\\u003cem\\u003eInterpretive note.\\u003c/em\\u003e The ranking C\\u0026thinsp;\\u0026gt;\\u0026thinsp;A\\u0026thinsp;\\u0026gt;\\u0026thinsp;B holds across both scenarios, indicating rank stability under reasonable re-weighting (see the sensitivity analysis below).\\u003c/p\\u003e\\n\\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e \\u003cstrong\\u003eWeight-sensitivity fan chart.\\u003c/strong\\u003e The DSSI ranges as (\\u0026alpha;,\\u0026beta;,\\u0026gamma;) vary within \\u0026plusmn;\\u0026thinsp;20% around the balanced scenario for each entity, highlighting rank stability.\\u003c/p\\u003e\\n\\u003ch3\\u003e5) Brief sensitivity analysis\\u003c/h3\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eWeight perturbations of \\u0026plusmn;\\u0026thinsp;10\\u0026ndash;20%\\u003c/strong\\u003e around the balanced scenario did not change the ranking.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePrevention sensitivity (L₃)\\u003c/strong\\u003e: Increasing \\u0026gamma; to 0.5 (university scenario) slightly reduced the gap between A and C (A: 0.613 \\u0026rarr; 0.599) without altering the ranking.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLeave-one-out sub-indicators\\u003c/strong\\u003e: Dropping \\u003cstrong\\u003eM4\\u003c/strong\\u003e (reasoned decisions) lowered L\\u003csub\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/sub\\u003e for all entities, but the relative effect was limited because (\\u003cstrong\\u003eM1\\u0026ndash;M2-M3\\u003c/strong\\u003e) accounted for 80% of the L\\u003csub\\u003e1\\u003c/sub\\u003e weight.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e: The DSSI exhibits relative stability across reasonable policy weight ranges, supporting its suitability as a comparative tool across varied application contexts (universities/municipalities/regulatory bodies).\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e7) Interpretive Reading of the Results\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cspan\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e1. Entity C\\u003c/strong\\u003e attained near-maximal scores across monitoring, response, and prevention, yielding a post-normalization \\u003cstrong\\u003eDSSI\\u0026thinsp;=\\u0026thinsp;1.00\\u003c/strong\\u003e. In practical terms, this reflects the following:\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSlower diffusion\\u003c/strong\\u003e of harmful content (high M1\\u003csup\\u003e\\u0026lowast;\\u003c/sup\\u003e),\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLower densities\\u003c/strong\\u003e of hate-speech reports and \\u003cstrong\\u003ehigher shares\\u003c/strong\\u003e of reasoned moderation decisions,\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFaster response times\\u003c/strong\\u003e, \\u003cstrong\\u003egreater appeal transparency\\u003c/strong\\u003e, and \\u003cstrong\\u003elower decision-reversal rates\\u003c/strong\\u003e,\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHigher MIL coverage/completion\\u003c/strong\\u003e and \\u003cstrong\\u003elarger knowledge gains\\u003c/strong\\u003e:\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cspan\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2. Entity A\\u003c/strong\\u003e shows \\u003cstrong\\u003ea mid-tier performance\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eStrengths on \\u003cstrong\\u003eM2/M3\\u003c/strong\\u003e and \\u003cstrong\\u003eR1/R2\\u003c/strong\\u003e,\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eA \\u003cstrong\\u003eneed to improve MIL coverage/completion (P1/P2)\\u003c/strong\\u003e to raise L\\u003csub\\u003e\\u003cstrong\\u003e3\\u003c/strong\\u003e\\u003c/sub\\u003e,\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eEnhancing \\u003cstrong\\u003eM4\\u003c/strong\\u003e (reason-giving) would \\u003cstrong\\u003elift L\\u003c/strong\\u003e\\u003csub\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/sub\\u003e at a relatively low cost.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cspan\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3. Entity B lags across all dimensions\\u003c/strong\\u003e; the fastest path to improvement involves the following:\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eReducing response time (R1)\\u003c/strong\\u003e and \\u003cstrong\\u003eincreasing appeal transparency (R2)\\u003c/strong\\u003e,\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eLaunching a \\u003cstrong\\u003eshort-cycle MIL program\\u003c/strong\\u003e targeting the most vulnerable cohorts to boost \\u003cstrong\\u003eP1, P2, and P3\\u003c/strong\\u003e levels.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003e1) What do the results mean in operational terms?\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe findings (Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) indicate that \\u003cb\\u003eaccelerating response time (R1)\\u003c/b\\u003e and \\u003cb\\u003eslowing the diffusion of harmful narratives (M1)\\u003c/b\\u003e are the two most influential levers for quickly improving DSSI. This aligns with basic safety logic: every hour of delay expands the footprint of impact, while every minute that delays reaching 50% of impressions buys time for correction and containment. Practically, investment in \\u003cb\\u003eautomated triage of report→decision pipelines\\u003c/b\\u003e and \\u003cb\\u003eprogrammed escalation for high-risk content\\u003c/b\\u003e translates directly into gains on (L\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e) and, in turn, into a higher overall index value.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e2) The effect of explanation and transparency (M4, R2)\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eA higher share of reasoned decisions and greater appeal transparency strengthen user trust and reduce the decision reversal rate in the medium term \\u003cb\\u003e(R3)\\u003c/b\\u003e. In comparative terms, Entity A can lift (L\\u003csub\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/sub\\u003e) relatively quickly by increasing the provision of \\u003cb\\u003estatements of reasons (M4)\\u003c/b\\u003e at a modest technical cost; however, the deeper impact comes from \\u003cb\\u003eshortening response times (R1)\\u003c/b\\u003e and \\u003cb\\u003eimproving the quality of appeal pathways (R2)\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3) Prevention is not a luxury—it is a force multiplier\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe “university” scenario (weighting (\\\\gamma = 0.5)) shows that the prevention layer (L\\u003csub\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/sub\\u003e) \\u003cb\\u003enarrows the gaps across entities\\u003c/b\\u003e. Expanding MIL coverage and completion and raising knowledge gains (P1–P3) improve audiences’ epistemic resilience and \\u003cb\\u003ereduce future complaint inflows\\u003c/b\\u003e. In other words, investing in digital-literacy programs is not merely an educational duty; it \\u003cb\\u003elowers downstream operational costs\\u003c/b\\u003e for monitoring and response.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e4) Sustaining improvement—balancing the three layers\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eImprovements in (L\\u003csub\\u003e1\\u003c/sub\\u003e) and (L\\u003csub\\u003e2\\u003c/sub\\u003e) can be achieved rapidly through tools and procedures, but they may be \\u003cb\\u003efragile\\u003c/b\\u003e unless underpinned by a preventive layer that raises public “immunity.” Conversely, prevention alone, without operational upgrades in monitoring and response, has a \\u003cb\\u003elimited impact during surges\\u003c/b\\u003e. By construction, the DSSI’s three-layer design helps decision-makers \\u003cb\\u003ebalance investments\\u003c/b\\u003e and \\u003cb\\u003etrack their effects over time\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e5) Portability and context\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eAltering weights within a reasonable policy range does not invert the ranking, suggesting index robustness. Nonetheless, contextual calibration is essential: \\u003cb\\u003euniversities\\u003c/b\\u003e may prioritize prevention weights, \\u003cb\\u003eand municipalities and interior ministries\\u003c/b\\u003e may up-weight monitoring and response. The DSSI supports \\u003cb\\u003eadjustable weights and benchmark thresholds\\u003c/b\\u003e while maintaining a transparent computation.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e6) Potential risks and misinterpretation\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eSmall differences in scores may be read as substantively meaningful when they fall within the \\u003cb\\u003euncertainty margin\\u003c/b\\u003e. We therefore recommend publishing the index with a \\u003cb\\u003efan chart\\u003c/b\\u003e, providing \\u003cb\\u003econfidence intervals or sensitivity ranges\\u003c/b\\u003e, and avoiding the use of the DSSI as a \\u003cb\\u003econtext-free “final rank.”\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLimitations\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eData quality heterogeneity across the platforms.\\u003c/b\\u003e Not all transparency reports share identical definitions or quality; \\u003cb\\u003etherefore, normalization and compensatory methods\\u003c/b\\u003e are required.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003ePartial reliance on secondary sources was observed.\\u003c/b\\u003e Some reports may lag or exhibit taxonomic gaps; the index is designed to \\u003cb\\u003eaccommodate interim proxies\\u003c/b\\u003e with explicit documentation of these gaps.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eIncomplete coverage of the harms.\\u003c/b\\u003e The index captures operationally measurable harms (latency, reports, appeals) and may not fully reflect \\u003cb\\u003efine-grained psychosocial dimensions\\u003c/b\\u003e without complementary field studies.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eInterdependence among the indicators.\\u003c/b\\u003e Some indicators may be causally linked (e.g., M1 and R1). We mitigated bias through \\u003cb\\u003eweight design\\u003c/b\\u003e and \\u003cb\\u003elayered interpretations\\u003c/b\\u003e of the outputs.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePractical Implications\\u003c/h2\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eFor universities/schools\\u003c/b\\u003e: Adopt a \\u003cb\\u003eDSSI-informed instructional dashboard\\u003c/b\\u003e to track MIL coverage/completion and pre/post-knowledge gains, and link these to internal monitoring metrics (complaints, response time).\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eFor municipalities/ministries\\u003c/b\\u003e: Integrate a \\u003cb\\u003eunified reporting portal\\u003c/b\\u003e and adopt \\u003cb\\u003ereason-giving templates\\u003c/b\\u003e with clear SLA timelines and periodic weight recalibration.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eFor platforms/partners\\u003c/b\\u003e: Publish \\u003cb\\u003estandardized transparency templates\\u003c/b\\u003e that facilitate comparison and provide \\u003cb\\u003eresearch-grade data access\\u003c/b\\u003e (metadata, unified definitions), directly improving (L\\u003csub\\u003e1\\u003c/sub\\u003e).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eImplementation Roadmap\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eBaseline establishment\\u003c/b\\u003e: The first cycle of M/R/P values was collected, normalization was applied, and the initial DSSI was published using a \\u003cb\\u003efan chart\\u003c/b\\u003e.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eRapid 90-day intervention\\u003c/b\\u003e: Shorten \\u003cb\\u003eR1\\u003c/b\\u003e, raise \\u003cb\\u003eR2/M4\\u003c/b\\u003e via reason-giving templates, and deploy \\u003cb\\u003eshort, intensive MIL sprints\\u003c/b\\u003e for the most vulnerable cohorts.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSemiannual review\\u003c/b\\u003e: Recompute the DSSI, publish \\u003cb\\u003etemporal comparisons\\u003c/b\\u003e (Δ per layer), and adjust weights to match the institutional strategy.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eContinuous improvement\\u003c/b\\u003e: Onboard new data sources (transparency APIs, authentication logs) and \\u003cb\\u003escale prevention\\u003c/b\\u003e, in line with observed knowledge gains.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusion and Recommendations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eWhat is new?\\u003c/strong\\u003e We introduced a composite, equation- and weight-transparent index to measure digital social safety across three layers: monitoring, response, and prevention. The DSSI bridges the gap between \\u003cstrong\\u003einformational-risk discourse\\u003c/strong\\u003e and \\u003cstrong\\u003einstitution-adoptable operational tools\\u003c/strong\\u003e, offering a decision dashboard for \\u003cstrong\\u003eprioritization\\u003c/strong\\u003e and \\u003cstrong\\u003eimpact evaluation\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eKey takeaways\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026middot; \\u003cstrong\\u003eShorter response times\\u003c/strong\\u003e and \\u003cstrong\\u003eslower harmful diffusion\\u003c/strong\\u003e deliver the \\u003cstrong\\u003elargest immediate gains\\u003c/strong\\u003e for the DSSI.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026middot; \\u003cstrong\\u003eMIL-based prevention\\u003c/strong\\u003e acts as a \\u003cstrong\\u003eforce multiplier\\u003c/strong\\u003e, stabilizing monitoring and response gains over the medium term.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026middot; The index is \\u003cstrong\\u003erobust\\u003c/strong\\u003e to reasonable weight shifts, but the results \\u003cstrong\\u003emust be accompanied by uncertainty displays\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eActionable recommendations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e1.\\u0026nbsp; \\u0026nbsp;Adopt \\u003cstrong\\u003ereason-giving templates\\u003c/strong\\u003e and a \\u003cstrong\\u003eclear SLA\\u003c/strong\\u003e for the report-decision-appeal pathway.\\u003c/p\\u003e\\n\\u003cp\\u003e2.\\u0026nbsp; \\u0026nbsp;Launch \\u003cstrong\\u003etargeted MIL programs\\u003c/strong\\u003e with measured pre/post \\u003cstrong\\u003eknowledge gains\\u003c/strong\\u003e integrated into the DSSI dashboard.\\u003c/p\\u003e\\n\\u003cp\\u003e3. \\u003cstrong\\u003eStandardize transparency templates\\u003c/strong\\u003e and expand \\u003cstrong\\u003eresearcher data access\\u003c/strong\\u003e to strengthen (L\\u003csub\\u003e1\\u003c/sub\\u003e) and reduce comparability gaps.\\u003c/p\\u003e\\n\\u003cp\\u003e4.\\u0026nbsp; \\u0026nbsp;The DSSI \\u003cstrong\\u003eshould be published periodically\\u003c/strong\\u003e with \\u003cstrong\\u003efan charts\\u003c/strong\\u003e and sensitivity analysis, and a balanced intervention roadmap should be pursued across all three layers.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFuture work:\\u003c/strong\\u003e Multi-site empirical validation, integration of \\u003cstrong\\u003eautomated data pipelines\\u003c/strong\\u003e, and refinement of weight models (e.g., \\u003cstrong\\u003einterpretable machine learning\\u003c/strong\\u003e) to evolve from policy-set weights to \\u003cstrong\\u003eimpact-based\\u003c/strong\\u003e weighting.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData \\u0026amp; Materials Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study relies exclusively on open secondary sources (DSA transparency reports, governance portals, policy documents and UNESCO MIL resources). All indicators, definitions, and weights are specified in the text and tables.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval and Consent\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study did not involve primary human subject data or experiments; it solely used open secondary sources and did not require ethical approval.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no financial or non-financial competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study did not receive funding from any external sources.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA. A: conceived and designed the study, developed the methodology, and wrote the manuscript. S. A.: Writing \\u0026ndash; review and editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAI Use Disclosure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTools were used to assist with the editing, drafting, and formatting. The authors reviewed all outputs and were responsible for their accuracy and content.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for the financial support (QU-APC-2025).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eEuropean Commission, \\u0026ldquo;How the Digital Services Act enhances transparency online,\\u0026rdquo; \\u003cem\\u003eShaping Europe\\u0026rsquo;s Digital Future\\u003c/em\\u003e, Sept. 24, 2025. (European Digital Strategy).\\u003c/li\\u003e\\n\\u003cli\\u003eP. Iamiceli, \\u0026ldquo;Online Platforms and the Digital Turn in EU Contract Law: Unfair Practices, Transparency and the (pierced) Veil of Digital Immunity,\\u0026rdquo; \\u003cem\\u003eEuropean Review of Contract Law\\u003c/em\\u003e, vol. 15, no. 4, pp. 392\\u0026ndash;420, 2019, doi: 10.1515/ercl-2019-0024. \\u003c/li\\u003e\\n\\u003cli\\u003eOECD, \\u003cem\\u003eFacts Not Fakes: Tackling Disinformation, Strengthening Information Integrity\\u003c/em\\u003e. Paris, France: OECD Publishing, 2024, doi: 10.1787/d909ff7a-en.\\u003c/li\\u003e\\n\\u003cli\\u003eC. Lu, B. Hu, M.-M. Bao, C. Wang, C. Bi, and X.-D. Ju, \\u0026ldquo;Can Media Literacy Intervention Improve Fake News Credibility Assessment? A Meta-Analysis,\\u0026rdquo; \\u003cem\\u003eCyberpsychology, Behavior, and Social Networking\\u003c/em\\u003e, vol. 27, no. 4, pp. 240\\u0026ndash;252, 2024, doi: 10.1089/cyber.2023.0324.\\u003c/li\\u003e\\n\\u003cli\\u003eA. Johnson and P. Roy, \\u003cem\\u003eEU Should Improve Transparency in the Digital Services Act\\u003c/em\\u003e. Washington, DC: Information Technology \\u0026amp; Innovation Foundation; 2025.\\u003c/li\\u003e\\n\\u003cli\\u003eM. Maroni, \\u0026ldquo;\\u0026lsquo;Mediated transparency\\u0026rsquo;: The Digital Services Act and the legitimisation of platform power,\\u0026rdquo; in \\u003cem\\u003e(In)visible European Government\\u003c/em\\u003e, Routledge, 2023. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.4324/9781003257936-19/mediated-transparency-marta-maroni\\u003c/li\\u003e\\n\\u003cli\\u003eM. Hillebrandt, P. Leino-Sandberg, and I. Koivisto, Eds., \\u003cem\\u003e(In)visible European Government: Critical Approaches to Transparency as an Ideal and a Practice\\u003c/em\\u003e, 1st ed. Routledge, 2023, doi: 10.4324/9781003257936.\\u003c/li\\u003e\\n\\u003cli\\u003eG. Huang, W. Jia, and W. Yu, \\u0026ldquo;Media Literacy Interventions Improve Resilience to Misinformation: A Meta-Analytic Investigation of Overall Effect and Moderating Factors,\\u0026rdquo; \\u003cem\\u003eCommunication Research\\u003c/em\\u003e, 2024, doi: 10.1177/00936502241288103.\\u003c/li\\u003e\\n\\u003cli\\u003eA. Strowel and J. De Meyere, \\u0026ldquo;The Digital Services Act: transparency as an efficient tool to curb the spread of disinformation on online platforms,\\u0026rdquo; \\u003cem\\u003eJIPITEC\\u003c/em\\u003e, vol. 14, p. 66, 2023. [Online]. Available: https://nbn-resolving.de/urn:nbn:de:0009-dppl-v3-en8\\u003c/li\\u003e\\n\\u003cli\\u003eC. Papaevangelou and Votta, \\u0026ldquo;Trading nuance for scale? Platform observability and content governance under the DSA,\\u0026rdquo; \\u003cem\\u003eInternet Policy Review\\u003c/em\\u003e, vol. 14, no. 3, 2025, doi: 10.14763/2025.3.2037.\\u003c/li\\u003e\\n\\u003cli\\u003eC. M. Pierson and E. Hildt, \\u0026ldquo;Reconcilable Differences: Comparative Analysis of EU and US Ethical AI Frameworks with Focus on Divergent Ethical Aspects,\\u0026rdquo; \\u003cem\\u003eProceedings of the Association for Information Science and Technology\\u003c/em\\u003e, vol. 62, no. 1, pp. 509\\u0026ndash;520, 2025, doi: 10.1002/pra2.1274.\\u003c/li\\u003e\\n\\u003cli\\u003eH. Zhao, J. Xu, T. O. Iyendo, O. D. Apuke, E. A. Tunca, and V. C. Gever, \\u0026ldquo;The effectiveness of using audio-visual based media intervention for promoting social media literacy skills to curtail fake news on social media: A quasi-experimental investigation,\\u0026rdquo; \\u003cem\\u003eInformation Development\\u003c/em\\u003e, vol. 41, no. 1, pp. 92\\u0026ndash;105, 2023, doi: 10.1177/02666669231217236.\\u003c/li\\u003e\\n\\u003cli\\u003eS. Johnson, \\u0026ldquo;From Gift to Governance: Commons Theory, Cultural Exchange, and Governance Friction in Public Institutions,\\u0026rdquo; SSRN preprint, Aug. 16, 2025. [Online]. Available: https://ssrn.com/abstract=5393963\\u003c/li\\u003e\\n\\u003cli\\u003eQ. Zhou, S. Wang, L. Wang, and W. Xu, \\u0026ldquo;Knowledge governance and innovation ambidexterity in the platform context: exploring the role of knowledge transformation,\\u0026rdquo; \\u003cem\\u003eJournal of Knowledge Management\\u003c/em\\u003e, vol. 29, no. 4, pp. 1301\\u0026ndash;1329, 2025, doi: 10.1108/JKM-03-2024-0256.\\u003c/li\\u003e\\n\\u003cli\\u003eK. S\\u0026ouml;derlund, \\u003cem\\u003eAI Transparency in Trustworthy AI: From Metaphor to Governance Tool in EU Technology Regulation\\u003c/em\\u003e. Ph.D. thesis, Dept. of Technology and Society, Lund Univ., Lund, Sweden, 2025. [Online]. Available: https://lup.lub.lu.se/search/files/218764646/KSo_derlund_PhD_Thesis_frame_electronic_version.pdf\\u003c/li\\u003e\\n\\u003cli\\u003eM. Monti, \\u0026ldquo;The EU Code of Practice on Disinformation and the Risk of the Privatisation of Censorship,\\u0026rdquo; in \\u003cem\\u003eDemocracy and Fake News\\u003c/em\\u003e, Routledge, 2020, pp. 214\\u0026ndash;225.\\u003c/li\\u003e\\n\\u003cli\\u003eS. M\\u0026uuml;ndges and K. Park, \\u0026ldquo;But did they really? Platforms\\u0026rsquo; compliance with the Code of Practice on Disinformation in review,\\u0026rdquo; \\u003cem\\u003eInternet Policy Review\\u003c/em\\u003e, vol. 13, no. 3, 2024, doi: 10.14763/2024.3.1786.\\u003c/li\\u003e\\n\\u003cli\\u003eM. L. Chiarella, \\u0026ldquo;Digital Markets Act (DMA) and Digital Services Act (DSA): New rules for the EU digital environment,\\u0026rdquo; \\u003cem\\u003eAthens Journal of Law\\u003c/em\\u003e, vol. 9, no. 1, pp. 33\\u0026ndash;58, Jan. 2023. [Online]. Available: https://heinonline.org/HOL/LandingPage?handle=hein.journals/atnsj9\\u0026amp;div=7\\u0026amp;id=\\u0026amp;page=\\u003c/li\\u003e\\n\\u003cli\\u003eJ. Ohnesorge, \\u0026ldquo;Counting without accountability? An analysis of the DSA\\u0026rsquo;s transparency reports,\\u0026rdquo; \\u003cem\\u003eDigital Society Blog\\u003c/em\\u003e, 2025. Zenodo, doi:10.5281/zenodo.17201618.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Social Security, Information Age, Developing, Social Safety, Policy Applications\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8001994/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8001994/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eWe present the Digital Social Safety Index (DSSI), a composite metric for assessing social safety in digital environments. The DSSI spans three layers: monitoring (disinformation velocity, hate speech reports, identity/authentication complaints, and reason-giving rates), response (time-to-decision, appeal transparency, reversal rates, and regulatory cooperation), and prevention (coverage, completion, and learning gains in digital/media literacy). Indicators are Min\\u0026ndash;Max normalized and aggregated with transparent within-layer weights and layer coefficients (α, β, γ), allowing context-specific calibration for universities, municipalities or ministries. Built entirely from open secondary sources (e.g., platform transparency reports and governance standards), the DSSI avoids bespoke data collection while remaining auditable. Simulations demonstrate that the DSSI captures temporal risk dynamics, discriminates among policy alternatives, and prioritizes actions through a dashboard. Sensitivity analyses show ranking stability under reasonable weight shifts while identifying high-leverage levers (e.g., reducing time-to-decision and slowing harmful diffusion). The contributions include a deployable measurement tool that links transparency obligations to prevention and a roadmap for validation, automated data streams, and sector-specific weighting.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Digital Social Safety Index (DSSI): A Transparent Composite Metric for Monitoring, Response, and Prevention in Digital Environments\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-10 05:16:56\",\"doi\":\"10.21203/rs.3.rs-8001994/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"1fbc391b-7a9f-46d2-9f9f-4cf66a6f35cd\",\"owner\":[],\"postedDate\":\"November 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-10T10:16:27+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-10 05:16:56\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8001994\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8001994\",\"identity\":\"rs-8001994\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}