Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 1 Dec 2022.
Builds on
14 of 34 references.
References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 479
- 2020The Pushshift Reddit Datasetcited by 289
- 2021XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyondcited by 114
- 2021SpanEmo: Casting Multi-label Emotion Classification as Span-predictioncited by 37
- 2015Quantifying Controversy in Social Mediacited by 28
- 2019Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Featurescited by 21
- 2021Detecting Community Sensitive Norm Violations in Online Conversationscited by 18
- 2022Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotioncited by 14
- 2017Conversation Modeling on Reddit using a Graph-Structured LSTMcited by 12
- 2018Explaining Controversy on Social Media via Stance Summarizationcited by 9
- 2020Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detectioncited by 8
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.