#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection

TL;DR

This paper investigates how social community features and diachronic stance evolution influence stance detection in online debates, specifically analyzing Twitter discussions on Brexit.

Contribution

It introduces a novel approach and annotation schema for stance detection that incorporates social network and temporal features.

Findings

Community features improve stance detection accuracy.

Diachronic analysis reveals opinion shifts over time.

Social context is crucial for understanding user stance.

Abstract

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.