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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.11138 |
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| _version_ | 1866915447421534208 |
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| author | Ma, Bin Zhang, Yifei Xian, Yongjin Li, Qi Zhou, Linna Miao, Gongxun |
| author_facet | Ma, Bin Zhang, Yifei Xian, Yongjin Li, Qi Zhou, Linna Miao, Gongxun |
| contents | With the rise of social media, rumor detection has drawn increasing attention. Although numerous methods have been proposed with the development of rumor classification datasets, they focus on identifying whether a post is a rumor, lacking the ability to mark the specific rumor content. This limitation largely stems from the lack of fine-grained marks in existing datasets. Constructing a rumor dataset with rumor content information marking is of great importance for fine-grained rumor identification. Such a dataset can facilitate practical applications, including rumor tracing, content moderation, and emergency response. Beyond being utilized for overall performance evaluation, this dataset enables the training of rumor detection algorithms to learn content marking, and thus improves their interpretability and reasoning ability, enabling systems to effectively address specific rumor segments. This paper constructs a dataset for rumor detection with fine-grained markings, named CLMIR (Content-Level Marking Dataset for Identifying Rumors). In addition to determining whether a post is a rumor, this dataset further marks the specific content upon which the rumor is based. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CLMIR: A Textual Dataset for Rumor Identification and Marking Ma, Bin Zhang, Yifei Xian, Yongjin Li, Qi Zhou, Linna Miao, Gongxun Computers and Society With the rise of social media, rumor detection has drawn increasing attention. Although numerous methods have been proposed with the development of rumor classification datasets, they focus on identifying whether a post is a rumor, lacking the ability to mark the specific rumor content. This limitation largely stems from the lack of fine-grained marks in existing datasets. Constructing a rumor dataset with rumor content information marking is of great importance for fine-grained rumor identification. Such a dataset can facilitate practical applications, including rumor tracing, content moderation, and emergency response. Beyond being utilized for overall performance evaluation, this dataset enables the training of rumor detection algorithms to learn content marking, and thus improves their interpretability and reasoning ability, enabling systems to effectively address specific rumor segments. This paper constructs a dataset for rumor detection with fine-grained markings, named CLMIR (Content-Level Marking Dataset for Identifying Rumors). In addition to determining whether a post is a rumor, this dataset further marks the specific content upon which the rumor is based. |
| title | CLMIR: A Textual Dataset for Rumor Identification and Marking |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2508.11138 |