# Explaining Controversy on Social Media via Stance Summarization

arXiv: https://arxiv.org/abs/1806.07942 · HTML: https://mlsanity.com/paper/1806.07942/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders](https://mlsanity.com/paper/2110.00210/) (2021)
- [Template-based Abstractive Microblog Opinion Summarisation](https://mlsanity.com/paper/2208.04083/) (2022)
- [Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration](https://mlsanity.com/paper/2402.14296/) (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)
- [Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning](https://mlsanity.com/paper/2210.03380/) (2022)
- [A Community-Based Approach for Stance Distribution and Argument Organization](https://mlsanity.com/paper/2604.16852/) (2026)

## 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.