# Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features

arXiv: https://arxiv.org/abs/1904.07372 · HTML: https://mlsanity.com/paper/1904.07372/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 21 of 21)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection](https://mlsanity.com/paper/2103.14916/) (2021)
- [Detecting Community Sensitive Norm Violations in Online Conversations](https://mlsanity.com/paper/2110.04419/) (2021)
- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [Countering Online Hate Speech: An NLP Perspective](https://mlsanity.com/paper/2109.02941/) (2021)
- [DebateQA: Evaluating Question Answering on Debatable Knowledge](https://mlsanity.com/paper/2408.01419/) (2024)
- [DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues](https://mlsanity.com/paper/2310.18130/) (2023)
- [Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey](https://mlsanity.com/paper/2503.01513/) (2025)
- [Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation](https://mlsanity.com/paper/2103.05345/) (2021)
- [How Did We Get Here? Summarizing Conversation Dynamics](https://mlsanity.com/paper/2404.19007/) (2024)
- [Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control](https://mlsanity.com/paper/2108.13556/) (2021)
- [Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit](https://mlsanity.com/paper/2212.00339/) (2022)
- [Conversation Modeling to Predict Derailment](https://mlsanity.com/paper/2303.11184/) (2023)
- [The Constant in HATE: Analyzing Toxicity in Reddit across Topics and Languages](https://mlsanity.com/paper/2404.18726/) (2024)
- [Conversations Gone Awry, But Then? Evaluating Conversational Forecasting Models](https://mlsanity.com/paper/2507.19470/) (2025)
- [Controversy Detection: a Text and Graph Neural Network Based Approach](https://mlsanity.com/paper/2112.11445/) (2021)
- [Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language](https://mlsanity.com/paper/2203.02392/) (2022)
- [Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters](https://mlsanity.com/paper/2212.13205/) (2022)
- [Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit](https://mlsanity.com/paper/2405.10213/) (2024)
- [From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework](https://mlsanity.com/paper/2605.02939/) (2026)

## Abstract (arXiv)

Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback. Our inclusion of the word "community" here is deliberate: what is controversial to some audiences may not be so to others. Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts, leveraging features drawn from both the textual content and the tree structure of the early comments that initiate the discussion. We find that even when only a handful of comments are available, e.g., the first 5 comments made within 15 minutes of the original post, discussion features often add predictive capacity to strong content-and-rate only baselines. Additional experiments on domain transfer suggest that conversation-structure features often generalize to other communities better than conversation-content features do.