Reading Between the Tweets: Deciphering Ideological Stances of
  Interconnected Mixed-Ideology Communities
Zihao He Ashwin Rao Siyi Guo Negar Mokhberian Kristina Lerman
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.