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.

翻译：自然语言处理的最新进展提升了解析在线社区微妙世界观的能力。现有针对意识形态立场探测的研究通常将自由派与保守派视为独立群体，但这种方法未能反映有机形成的在线社区的复杂观点及其相互关联。本文以2020年美国大选期间的推特讨论为研究对象，识别复杂交互社区。利用这种互联性，我们提出了一种新颖方法，在微调语言模型时融入消息传递机制，以探测这些社区的细微意识形态。通过比较语言模型生成的回应与真实世界调查结果，本方法在一致性上优于现有基线，揭示了语言模型在揭示互联混合意识形态社区内部及跨社区的复杂意识形态方面的潜力。