On Context-aware Detection of Cherry-picking in News Reporting
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.
Keywords
Cite
@article{arxiv.2401.05650,
title = {On Context-aware Detection of Cherry-picking in News Reporting},
author = {Israa Jaradat and Haiqi Zhang and Chengkai Li},
journal= {arXiv preprint arXiv:2401.05650},
year = {2024}
}