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

arXiv: https://arxiv.org/abs/2007.14936 · HTML: https://mlsanity.com/paper/2007.14936/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)
- [Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models](https://mlsanity.com/paper/2210.05715/) (2022)
- [Robust Stance Detection: Understanding Public Perceptions in Social Media](https://mlsanity.com/paper/2309.15176/) (2023)
- [UstanceBR: a social media language resource for stance prediction](https://mlsanity.com/paper/2312.06374/) (2023)

## Abstract (arXiv)

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.