Abstract
We consider how to credibly and reliably assess the opinions of individuals using their social media posts. To this end, this paper makes three contributions. First, we assemble a workflow and approach to applying modern natural language processing (NLP) methods to multi-target user stance detection in the wild. Second, we establish why the multi-target modeling of user stance is qualitatively more complicated than uni-target user-stance detection. Finally, we validate our method by showing how multi-dimensional measurement of user opinions not only reproduces known opinion polling results, but also enables the study of opinion dynamics at high levels of temporal and semantic resolution.- Anthology ID:
- 2024.wassa-1.16
- Volume:
- Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Orphée De Clercq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, Shabnam Tafreshi
- Venues:
- WASSA | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 200–214
- Language:
- URL:
- https://aclanthology.org/2024.wassa-1.16
- DOI:
- Cite (ACL):
- Benjamin Steel and Derek Ruths. 2024. Multi-Target User Stance Discovery on Reddit. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 200–214, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Target User Stance Discovery on Reddit (Steel & Ruths, WASSA-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.wassa-1.16.pdf