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

Zihao He, Ashwin Rao, Siyi Guo, Negar Mokhberian, Kristina Lerman
2/2/2024
cs.CLcs.CYcs.SI

No official code found. Best implementation: zihaohe123/communitylm-message-passing (0 stars, MIT, last commit 2025-03-03).

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.

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Code Implementations(1)

zihaohe123/communitylm-message-passingCommunity100%
MIT
00PythonJan 31, 20241 years agoMIT

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Cite this paper

@article{he2024reading,
  title  = {Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities},
  author = {Zihao He and Ashwin Rao and Siyi Guo and Negar Mokhberian and Kristina Lerman},
  year   = {2024},
  eprint = {2402.01091},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.01091v1}
}

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