# TAR on Social Media: A Framework for Online Content Moderation

arXiv: https://arxiv.org/abs/2108.12752 · HTML: https://mlsanity.com/paper/2108.12752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Models](https://mlsanity.com/paper/2212.00668/) (2022)
- [Transductive Model Selection under Prior Probability Shift](https://mlsanity.com/paper/2507.22647/) (2025)

## Abstract (arXiv)

Content moderation (removing or limiting the distribution of posts based on their contents) is one tool social networks use to fight problems such as harassment and disinformation. Manually screening all content is usually impractical given the scale of social media data, and the need for nuanced human interpretations makes fully automated approaches infeasible. We consider content moderation from the perspective of technology-assisted review (TAR): a human-in-the-loop active learning approach developed for high recall retrieval problems in civil litigation and other fields. We show how TAR workflows, and a TAR cost model, can be adapted to the content moderation problem. We then demonstrate on two publicly available content moderation data sets that a TAR workflow can reduce moderation costs by 20% to 55% across a variety of conditions.