mlsanityData to 7 Oct 2026

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

arXiv 2402.01091 ↗First posted 2 Feb 2024cs.CL cs.CY cs.SIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

16 of 39 references.

References · 16

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4 more
  1. 2022Infusing Knowledge from Wikipedia to Enhance Stance Detectioncited by 20
  2. 2020#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Electioncited by 17
  3. 2020Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spacescited by 10
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Cited by year

Citing papers per year: 2024: 5; 2025: 2.

7 papers.

  1. 2024How Susceptible are Large Language Models to Ideological Manipulation?cited by 26
  2. 2024Whose Emotions and Moral Sentiments Do Language Models Reflect?cited by 20
  3. 2024COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communitiescited by 12
  4. 2024Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forumscited by 3
  5. 2025A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Modelscited by 3
  6. 2024Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communitiescited by 2
  7. 2025LASTIST: LArge-Scale Target-Independent STance datasetcited by 0