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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.05650 |
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| _version_ | 1866914878584782848 |
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| author | Jaradat, Israa Zhang, Haiqi Li, Chengkai |
| author_facet | Jaradat, Israa Zhang, Haiqi Li, Chengkai |
| contents | Cherry-picking refers to the deliberate selection of evidence or facts that favor a particular viewpoint while ignoring or distorting evidence that supports an opposing perspective. Manually identifying cherry-picked statements in news stories can be challenging. In this study, we introduce a novel approach to detecting cherry-picked statements by identifying missing important statements in a target news story using language models and contextual information from other news sources. Furthermore, this research introduces a novel dataset specifically designed for training and evaluating cherry-picking detection models. Our best performing model achieves an F-1 score of about 89% in detecting important statements. Moreover, results show the effectiveness of incorporating external knowledge from alternative narratives when assessing statement importance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Context-aware Detection of Cherry-picking in News Reporting Jaradat, Israa Zhang, Haiqi Li, Chengkai Computation and Language Cherry-picking refers to the deliberate selection of evidence or facts that favor a particular viewpoint while ignoring or distorting evidence that supports an opposing perspective. Manually identifying cherry-picked statements in news stories can be challenging. In this study, we introduce a novel approach to detecting cherry-picked statements by identifying missing important statements in a target news story using language models and contextual information from other news sources. Furthermore, this research introduces a novel dataset specifically designed for training and evaluating cherry-picking detection models. Our best performing model achieves an F-1 score of about 89% in detecting important statements. Moreover, results show the effectiveness of incorporating external knowledge from alternative narratives when assessing statement importance. |
| title | On Context-aware Detection of Cherry-picking in News Reporting |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.05650 |