Commenotes: Synthesizing Organic Comments to Support Community-Based Fact-Checking
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909786575994880 |
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| author | Zhang, Shuning Wang, Linzhi Shi, Dai Chuai, Yuwei Chen, Jingruo Chen, Yunyi Wang, Yifan Wang, Yating Yi, Xin Li, Hewu |
| author_facet | Zhang, Shuning Wang, Linzhi Shi, Dai Chuai, Yuwei Chen, Jingruo Chen, Yunyi Wang, Yifan Wang, Yating Yi, Xin Li, Hewu |
| contents | Community-based fact-checking is promising to reduce the spread of misleading posts at scale. However, its effectiveness can be undermined by the delays in fact-check delivery. Notably, user-initiated organic comments often contain debunking information and have the potential to help mitigate this limitation. Here, we investigate the feasibility of synthesizing comments to generate timely high-quality fact-checks. To this end, we analyze over 2.2 million replies on X and introduce Commenotes, a two-phase framework that filters and synthesizes comments to facilitate fact-check delivery. Our framework reveals that fact-checking comments appear early and sufficiently: 99.3\% of misleading posts receive debunking comments within the initial two hours since post publication, with synthesized \textit{commenotes} successfully earning user trust for 85.8\% of those posts. Additionally, a user study (N=144) found that the synthesized commenotes were often preferred, with the best-performing model achieving a 70.1\% win rate over human notes and being rated as significantly more helpful. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Commenotes: Synthesizing Organic Comments to Support Community-Based Fact-Checking Zhang, Shuning Wang, Linzhi Shi, Dai Chuai, Yuwei Chen, Jingruo Chen, Yunyi Wang, Yifan Wang, Yating Yi, Xin Li, Hewu Human-Computer Interaction Community-based fact-checking is promising to reduce the spread of misleading posts at scale. However, its effectiveness can be undermined by the delays in fact-check delivery. Notably, user-initiated organic comments often contain debunking information and have the potential to help mitigate this limitation. Here, we investigate the feasibility of synthesizing comments to generate timely high-quality fact-checks. To this end, we analyze over 2.2 million replies on X and introduce Commenotes, a two-phase framework that filters and synthesizes comments to facilitate fact-check delivery. Our framework reveals that fact-checking comments appear early and sufficiently: 99.3\% of misleading posts receive debunking comments within the initial two hours since post publication, with synthesized \textit{commenotes} successfully earning user trust for 85.8\% of those posts. Additionally, a user study (N=144) found that the synthesized commenotes were often preferred, with the best-performing model achieving a 70.1\% win rate over human notes and being rated as significantly more helpful. |
| title | Commenotes: Synthesizing Organic Comments to Support Community-Based Fact-Checking |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.11052 |