#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
This work addresses the problem of more accurately detecting user stances in social media debates for researchers and analysts, though it is incremental by building on existing stance detection methods with new features.
The study tackled stance detection in online political debates by incorporating social community and diachronic evolution features, specifically analyzing the UK Brexit referendum on Twitter, and found that these features significantly improved classification performance.
Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users' opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users' opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance.