# Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support

arXiv: https://arxiv.org/abs/2211.16525 · HTML: https://mlsanity.com/paper/2211.16525/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 70 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [A Just and Comprehensive Strategy for Using NLP to Address Online Abuse](https://mlsanity.com/paper/1906.01738/) (2019)
- [Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop](https://mlsanity.com/paper/1909.01362/) (2019)
- [Forecasting the presence and intensity of hostility on Instagram using linguistic and social features](https://mlsanity.com/paper/1804.06759/) (2018)
- [Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community](https://mlsanity.com/paper/1910.09563/) (2019)
- [Trajectories of Blocked Community Members: Redemption, Recidivism and Departure](https://mlsanity.com/paper/1902.08628/) (2019)

## Built on by (most cited first, 15 of 15)

- [Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey](https://mlsanity.com/paper/2503.01513/) (2025)
- [DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship](https://mlsanity.com/paper/2411.01844/) (2024)
- [Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models](https://mlsanity.com/paper/2402.03284/) (2024)
- [How Did We Get Here? Summarizing Conversation Dynamics](https://mlsanity.com/paper/2404.19007/) (2024)
- [Watching the Watchers: A Comparative Fairness Audit of Cloud-based Content Moderation Services](https://mlsanity.com/paper/2406.14154/) (2024)
- [ArgCMV: An Argument Summarization Benchmark for the LLM-era](https://mlsanity.com/paper/2508.19580/) (2025)
- [Social Orientation: A New Feature for Dialogue Analysis](https://mlsanity.com/paper/2403.04770/) (2024)
- [Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation](https://mlsanity.com/paper/2503.16505/) (2025)
- [Conversations Gone Awry, But Then? Evaluating Conversational Forecasting Models](https://mlsanity.com/paper/2507.19470/) (2025)
- [Conversation Derailment Forecasting with Graph Convolutional Networks](https://mlsanity.com/paper/2306.12982/) (2023)
- [Assessing and Refining ChatGPT's Performance in Identifying Targeting and Inappropriate Language: A Comparative Study](https://mlsanity.com/paper/2505.21710/) (2025)
- [Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations](https://mlsanity.com/paper/2507.21089/) (2025)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Wait! There's a Way Out: A Decision Mechanism for Forecasting Conversational Derailment](https://mlsanity.com/paper/2605.29243/) (2026)
- [Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online](https://mlsanity.com/paper/2609.00808/) (2026)

## Abstract (arXiv)

To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. This reactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms -- such as proactively discouraging the emergence of antisocial behavior rather than reacting to it -- and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow.