On Context-aware Detection of Cherry-picking in News Reporting
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 45 references that are in the map. First posted on arXiv 11 Jan 2024.
Builds on
9 of 45 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2020Longformer: The Long-Document Transformercited by 3,362
- 2020We Can Detect Your Bias: Predicting the Political Ideology of News Articlescited by 92
- 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
- 2020The COVID-19 Social Media Infodemiccited by 64
- 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 54
- 2020Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularitycited by 32
- 2021Political Bias and Factualness in News Sharing across more than 100,000 Online Communitiescited by 9
Built on by
Citing papers per year: 2024: 1.
1 paper.