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The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media

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arxiv 2312.10269 v4 pith:MPQW5MP5 submitted 2023-12-16 cs.SI cs.AIcs.CYcs.HC

classification cs.SIcs.AIcs.CYcs.HC
keywords databaseplatformsmoderationactionsdataonlinetransparencymedia
verification ladder T0 review T1 audit T2 compute T3 formal
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Since September 2023, the Digital Services Act (DSA) obliges large online platforms to submit detailed data on each moderation action they take within the European Union (EU) to the DSA Transparency Database. From its inception, this centralized database has sparked scholarly interest as an unprecedented and potentially unique trove of data on real-world online moderation. Here, we thoroughly analyze all 353.12M records submitted by the eight largest social media platforms in the EU during the first 100 days of the database. Specifically, we conduct a platform-wise comparative study of their: volume of moderation actions, grounds for decision, types of applied restrictions, types of moderated content, timeliness in undertaking and submitting moderation actions, and use of automation. Furthermore, we systematically cross-check the contents of the database with the platforms' own transparency reports. Our analyses reveal that (i) the platforms adhered only in part to the philosophy and structure of the database, (ii) the structure of the database is partially inadequate for the platforms' reporting needs, (iii) the platforms exhibited substantial differences in their moderation actions, (iv) a remarkable fraction of the database data is inconsistent, (v) the platform X (formerly Twitter) presents the most inconsistencies. Our findings have far-reaching implications for policymakers and scholars across diverse disciplines. They offer guidance for future regulations that cater to the reporting needs of online platforms in general, but also highlight opportunities to improve and refine the database itself.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A tri-partite survey finds that hate speech definitions and moderation practices in country laws, platform policies, and NLP datasets are largely misaligned, and calls for a unified proactive moderation framework.

  2. From Reports to Reality: Testing Consistency in Instagram's Digital Services Act Compliance Data

    cs.CY 2025-07 conditional novelty 5.0 of 10

    An analysis of Instagram's DSA filings finds major arithmetic, cross-mechanism, and historical inconsistencies that undermine the reliability of its compliance reporting.

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