On Detecting Cherry-picking in News Coverage Using Large Language Models

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 instances of cherry-picked statements in news stories can be challenging, particularly when the opposing viewpoint's story is absent. This study introduces Cherry, an innovative approach for automatically detecting cherry-picked statements in news articles by finding missing important statements in the target news story. Cherry utilizes the analysis of news coverage from multiple sources to identify instances of cherry-picking. Our approach relies on language models that consider contextual information from other news sources to classify statements based on their importance to the event covered in the target news story. Furthermore, this research introduces a novel dataset specifically designed for cherry-picking detection, which was used to train and evaluate the performance of the models. Our best performing model achieves an F-1 score of about %89 in detecting important statements when tested on unseen set of news stories. Moreover, results show the importance incorporating external knowledge from alternative unbiased narratives when assessing a statement's importance.