Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
This work addresses the challenge of deciphering complex ideologies in interconnected mixed-ideology communities for researchers and practitioners in NLP and social science, representing an incremental improvement over prior methods that treat groups as separate.
The paper tackled the problem of understanding nuanced ideological stances in online communities by studying Twitter discussions of the 2020 U.S. election, introducing a method that uses message passing during LM fine-tuning to probe ideologies, resulting in higher alignment with real-world survey results compared to existing baselines.
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.