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arXiv · 2401.05650

On Context-aware Detection of Cherry-picking in News Reporting

Israa Jaradat·Haiqi Zhang·Chengkai Li
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Abstract

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.

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Israa Jaradat, Haiqi Zhang, Chengkai Li. 2024-01-11. On Context-aware Detection of Cherry-picking in News Reporting. https://arxiv.org/abs/2401.05650

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