# Quantifying Feature Importance for Online Content Moderation

arXiv: https://arxiv.org/abs/2510.19882 · HTML: https://mlsanity.com/paper/2510.19882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 47 of its 101 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 30 of 47)

- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets](https://mlsanity.com/paper/2410.07991/) (2024)
- [Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models](https://mlsanity.com/paper/2502.18695/) (2025)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [SLM-Mod: Small Language Models Surpass LLMs at Content Moderation](https://mlsanity.com/paper/2410.13155/) (2024)
- [FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding](https://mlsanity.com/paper/2002.08608/) (2020)
- [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)
- [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://mlsanity.com/paper/2412.07338/) (2024)
- [QuaPy: A Python-Based Framework for Quantification](https://mlsanity.com/paper/2106.11057/) (2021)
- [Diversity in Sociotechnical Machine Learning Systems](https://mlsanity.com/paper/2107.09163/) (2021)
- [Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald](https://mlsanity.com/paper/2201.06455/) (2022)
- [Outcome-Constrained Large Language Models for Countering Hate Speech](https://mlsanity.com/paper/2403.17146/) (2024)
- [STAND-Guard: A Small Task-Adaptive Content Moderation Model](https://mlsanity.com/paper/2411.05214/) (2024)
- [Class Prior Estimation under Covariate Shift: No Problem?](https://mlsanity.com/paper/2206.02449/) (2022)
- [The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media](https://mlsanity.com/paper/2312.10269/) (2023)
- [Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach](https://mlsanity.com/paper/2109.08549/) (2021)
- [Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack](https://mlsanity.com/paper/2302.04450/) (2023)
- [LAMBRETTA: Learning to Rank for Twitter Soft Moderation](https://mlsanity.com/paper/2212.05926/) (2022)
- [Kernel Density Estimation for Multiclass Quantification](https://mlsanity.com/paper/2401.00490/) (2023)
- [Re-Assessing the "Classify and Count" Quantification Method](https://mlsanity.com/paper/2011.02552/) (2020)
- [Binary Quantification and Dataset Shift: An Experimental Investigation](https://mlsanity.com/paper/2310.04565/) (2023)
- [Detection and Characterization of Coordinated Online Behavior: A Survey](https://mlsanity.com/paper/2408.01257/) (2024)
- [Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations](https://mlsanity.com/paper/2106.03952/) (2021)
- [When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban](https://mlsanity.com/paper/2106.16207/) (2021)
- [A Year of the DSA Transparency Database: What it (Does Not) Reveal About Platform Moderation During the 2024 European Parliament Election](https://mlsanity.com/paper/2504.06976/) (2025)

## Abstract (arXiv)

Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear understanding of which user characteristics are associated with different behavioural responses, which is the goal of this work. We investigate the informativeness of 753 socio-behavioural, linguistic, relational, and psychological features, in predicting the behavioural changes of 16.8K users affected by a major moderation intervention on Reddit. To reach this goal, we frame the problem in terms of "quantification", a task well-suited to estimating shifts in aggregate user behaviour. We then apply a greedy feature selection strategy with the double goal of (i) identifying the features that are most predictive of changes in user activity, toxicity, and participation diversity, and (ii) estimating their importance. Our results allow identifying a small set of features that are consistently informative across all tasks, and determining that many others are either task-specific or of limited utility altogether. We also find that predictive performance varies according to the task, with changes in activity and toxicity being easier to estimate than changes in diversity. Overall, our results pave the way for the development of accurate systems that predict user reactions to moderation interventions. Furthermore, our findings highlight the complexity of post-moderation user behaviour, and indicate that effective moderation should be tailored not only to user traits but also to the specific objective of the intervention.