# Personalized Interventions for Online Moderation

arXiv: https://arxiv.org/abs/2205.09462 · HTML: https://mlsanity.com/paper/2205.09462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 10 of 10)

- [Generating Counter Narratives against Online Hate Speech: Data and Strategies](https://mlsanity.com/paper/2004.04216/) (2020)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit](https://mlsanity.com/paper/2009.11483/) (2020)
- [Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels](https://mlsanity.com/paper/2010.10397/) (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)
- [Debunking in a World of Tribes](https://mlsanity.com/paper/1510.04267/) (2015)
- ["I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter](https://mlsanity.com/paper/2101.07183/) (2021)
- [The Aftermath of Disbanding an Online Hateful Community](https://mlsanity.com/paper/1804.07354/) (2018)
- [My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social Networks](https://mlsanity.com/paper/2009.10802/) (2020)

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

- [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://mlsanity.com/paper/2412.07338/) (2024)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)

## Abstract (arXiv)

Current online moderation follows a one-size-fits-all approach, where each intervention is applied in the same way to all users. This naive approach is challenged by established socio-behavioral theories and by recent empirical results that showed the limited effectiveness of such interventions. We propose a paradigm-shift in online moderation by moving towards a personalized and user-centered approach. Our multidisciplinary vision combines state-of-the-art theories and practices in diverse fields such as computer science, sociology and psychology, to design personalized moderation interventions (PMIs). In outlining the path leading to the next-generation of moderation interventions, we also discuss the most prominent challenges introduced by such a disruptive change.