Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities

Zihao He, Ashwin Rao, Siyi Guo, Negar Mokhberian, Kristina Lerman


Abstract
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberals and conservatives as separate groups. However, this fails to account for the nuanced views of the organically formed online communities and the connections between them. In this paper, we study discussions of the 2020 U.S. election on Twitter to identify complex interacting communities. Capitalizing on this interconnectedness, we introduce a novel approach that harnesses message passing when finetuning language models (LMs) to probe the nuanced ideologies of these communities. By comparing the responses generated by LMs and real-world survey results, our method shows higher alignment than existing baselines, highlighting the potential of using LMs in revealing complex ideologies within and across interconnected mixed-ideology communities.
Anthology ID:
2024.findings-eacl.104
Volume:
Findings of the Association for Computational Linguistics: EACL 2024
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1523–1536
Language:
URL:
https://aclanthology.org/2024.findings-eacl.104
DOI:
Bibkey:
Cite (ACL):
Zihao He, Ashwin Rao, Siyi Guo, Negar Mokhberian, and Kristina Lerman. 2024. Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1523–1536, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities (He et al., Findings 2024)
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https://aclanthology.org/2024.findings-eacl.104.pdf
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