Explaining Controversy on Social Media via Stance Summarization
This addresses the challenge for social media users in manually navigating overwhelming content to learn about controversies, though it is incremental as it builds on existing work in controversy identification.
The paper tackles the problem of understanding controversies on social media by generating stance-aware summaries that collect arguments from conflicting stances, and it shows that their method consistently outperforms baseline techniques in user evaluations on five Twitter topics.
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.