Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911769614614528 |
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| author | He, Zihao Rao, Ashwin Guo, Siyi Mokhberian, Negar Lerman, Kristina |
| author_facet | He, Zihao Rao, Ashwin Guo, Siyi Mokhberian, Negar Lerman, Kristina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities He, Zihao Rao, Ashwin Guo, Siyi Mokhberian, Negar Lerman, Kristina Computation and Language Computers and Society Social and Information Networks 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. |
| title | Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities |
| topic | Computation and Language Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2402.01091 |