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

arXiv: https://arxiv.org/abs/2312.10269 · HTML: https://mlsanity.com/paper/2312.10269/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Automatic Evaluation and Moderation of Open-domain Dialogue Systems](https://mlsanity.com/paper/2111.02110/) (2021)
- [Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit](https://mlsanity.com/paper/2009.11483/) (2020)
- [Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald](https://mlsanity.com/paper/2201.06455/) (2022)
- [Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content](https://mlsanity.com/paper/2112.00773/) (2021)
- ["I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter](https://mlsanity.com/paper/2101.07183/) (2021)
- [Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media](https://mlsanity.com/paper/2101.11824/) (2021)
- [How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events](https://mlsanity.com/paper/2209.07614/) (2022)
- [Content Moderation on Social Media in the EU: Insights From the DSA Transparency Database](https://mlsanity.com/paper/2312.04431/) (2023)
- [One of Many: Assessing User-level Effects of Moderation Interventions on r/The_Donald](https://mlsanity.com/paper/2209.08809/) (2022)
- [Setting the Record Straighter on Shadow Banning](https://mlsanity.com/paper/2012.05101/) (2020)

## Built on by (most cited first, 10 of 10)

- [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://mlsanity.com/paper/2412.07338/) (2024)
- [HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation](https://mlsanity.com/paper/2507.04350/) (2025)
- ["Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System](https://mlsanity.com/paper/2606.13071/) (2026)
- [Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization](https://mlsanity.com/paper/2609.10410/) (2026)
- [Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management](https://mlsanity.com/paper/2406.19543/) (2024)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)
- [A Benchmark for Strategic Auditee Gaming Under Continuous Compliance Monitoring](https://mlsanity.com/paper/2605.06340/) (2026)
- [Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech](https://mlsanity.com/paper/2607.26236/) (2026)
- [FairMon: A Tool for Monitoring and Visualizing Algorithmic Fairness](https://mlsanity.com/paper/2609.26123/) (2026)

## Abstract (arXiv)

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.