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

Anonymous

Abstract: Online communities provide a platform for sharing ideas and information. Recent advances in NLP have driven interest in understanding the ideological nuances between these communities. Existing research has focused on probing the views of liberals and conservatives, treating them 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 use 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 in finetuning language models to probe the nuanced ideologies of these communities. Extensive experiments demonstrate that our proposed method consistently outperform existing baselines, highlighting the potential of using language models in revealing complex ideologies within and across online communities.

Paper Type: long

Research Area: Computational Social Science and Cultural Analytics

Contribution Types: NLP engineering experiment, Data analysis

Languages Studied: English