Explaining Controversy on Social Media via Stance Summarization
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Abstract (arXiv)
In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In this light, there has been significant work that studies how to identify and measure controversy online. However, we currently lack a tool for effectively understanding controversy in social media. For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy. In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. We formalize the characteristics of a good stance summary and propose a ranking model accordingly. We first evaluate our methods on five controversial topics on Twitter. Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2018.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
9 papers.
- 2023Stance Detection with Collaborative Role-Infused LLM-Based Agentscited by 53
- 2022Infusing Knowledge from Wikipedia to Enhance Stance Detectioncited by 20
- 2021Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoderscited by 12
- 2022Template-based Abstractive Microblog Opinion Summarisationcited by 9
- 2024Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibrationcited by 5
- 2021Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Controlcited by 4
- 2022Anger Breeds Controversy: Analyzing Controversy and Emotions on Redditcited by 3
- 2022Zero-shot stance detection based on cross-domain feature enhancement by contrastive learningcited by 1
- 2026A Community-Based Approach for Stance Distribution and Argument Organizationcited by 0
- 2026A Community-Based Approach for Stance Distribution and Argument Organization18 Apr 2026
- 2024Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration22 Feb 2024
- 2023Stance Detection with Collaborative Role-Infused LLM-Based Agents16 Oct 2023
- 2022Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit1 Dec 2022
- 2022Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning7 Oct 2022
- 2022Template-based Abstractive Microblog Opinion Summarisation8 Aug 2022
- 2022Infusing Knowledge from Wikipedia to Enhance Stance Detection8 Apr 2022
- 2021Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders1 Oct 2021
- 2021Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control30 Aug 2021