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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.10511 |
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| _version_ | 1866913800856272896 |
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| author | Zhu, Zhengyuan Zhang, Haiqi Zhang, Zeyu Li, Chengkai |
| author_facet | Zhu, Zhengyuan Zhang, Haiqi Zhang, Zeyu Li, Chengkai |
| contents | Factual claims and misinformation circulate widely on social media and affect how people form opinions and make decisions. This paper presents a truthfulness stance map (TrustMap), an application that identifies and maps public stances toward factual claims across U.S. regions. Each social media post is classified as positive, negative, or neutral/no stance, based on whether it believes a factual claim is true or false, expresses uncertainty about the truthfulness, or does not explicitly take a position on the claim's truthfulness. The tool uses a retrieval-augmented model with fine-tuned language models for automatic stance classification. The stance classification results and social media posts are grouped by location to show how stance patterns vary geographically. TrustMap allows users to explore these patterns by claim and region and connects stance detection with geographical analysis to better understand public engagement with factual claims. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrustMap: Mapping Truthfulness Stance of Social Media Posts on Factual Claims for Geographical Analysis Zhu, Zhengyuan Zhang, Haiqi Zhang, Zeyu Li, Chengkai Social and Information Networks Factual claims and misinformation circulate widely on social media and affect how people form opinions and make decisions. This paper presents a truthfulness stance map (TrustMap), an application that identifies and maps public stances toward factual claims across U.S. regions. Each social media post is classified as positive, negative, or neutral/no stance, based on whether it believes a factual claim is true or false, expresses uncertainty about the truthfulness, or does not explicitly take a position on the claim's truthfulness. The tool uses a retrieval-augmented model with fine-tuned language models for automatic stance classification. The stance classification results and social media posts are grouped by location to show how stance patterns vary geographically. TrustMap allows users to explore these patterns by claim and region and connects stance detection with geographical analysis to better understand public engagement with factual claims. |
| title | TrustMap: Mapping Truthfulness Stance of Social Media Posts on Factual Claims for Geographical Analysis |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2504.10511 |