# Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit

arXiv: https://arxiv.org/abs/2212.00339 · HTML: https://mlsanity.com/paper/2212.00339/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features](https://mlsanity.com/paper/1904.07372/) (2019)
- [Detecting Community Sensitive Norm Violations in Online Conversations](https://mlsanity.com/paper/2110.04419/) (2021)
- [Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion](https://mlsanity.com/paper/2210.15842/) (2022)
- [Conversation Modeling on Reddit using a Graph-Structured LSTM](https://mlsanity.com/paper/1704.02080/) (2017)
- [Explaining Controversy on Social Media via Stance Summarization](https://mlsanity.com/paper/1806.07942/) (2018)
- [Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection](https://mlsanity.com/paper/2005.07886/) (2020)
- [Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats](https://mlsanity.com/paper/2211.00171/) (2022)
- [Controversy and Sentiment in Online News](https://mlsanity.com/paper/1409.8152/) (2014)

## Built on by (most cited first, 3 of 3)

- [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)
- [Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL](https://mlsanity.com/paper/2503.03654/) (2025)

## Abstract (arXiv)

Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has not been explored. To address this gap, we study controversy on Reddit, a popular network of online discussion forums. We collect discussions from a wide variety of topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc. Our study has three main findings. First, controversial comments express more anger and less admiration, joy and optimism than non-controversial comments. Second, controversial comments affect emotions of downstream comments in a discussion, usually resulting in long-term increase in anger and a decrease in positive emotions, although the magnitude and direction of emotional change depends on the forum. Finally, we show that emotions help better predict which comments will become controversial. Understanding emotional dynamics of online discussions can help communities to better manage conversations.