Feedback from reporting into platform algorithms

Ascertain whether and how patterns in Trusted Flagger and user reporting are used to train, tune, or otherwise influence platform recommendation, detection, or moderation algorithms, and determine the measurable impact of such feedback on platform outcomes.

Background

Understanding whether reporting signals feed back into platform algorithms is crucial for evaluating the systemic effects of TF activities and for assessing transparency and accountability under the DSA.

If reports influence algorithmic systems, it becomes necessary to measure resultant changes in content visibility, takedown rates, and user experience, and to ensure such feedback loops align with legal and fairness requirements.

References

One concern this creates is, if it is not clear whether reporting patterns are feeding back into algorithms, will it be clear how reporting is resulting in identifiable change on the platforms?

— "There is literally zero funding": Understanding the Emerging Role of Trusted Flaggers under the EU Digital Services Act  (2603.29874 - Sekwenz et al., 31 Mar 2026) in Discussion (The opaque nature of large platforms and providers)