REVIEW 1 cited by
On Context-aware Detection of Cherry-picking in News Reporting
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval
NEWSCOPE adds sentence-level clustering and cluster-aware greedy reranking to dense news retrieval, reporting higher viewpoint diversity on two new benchmarks, at a small relevance cost.
Discussion (0). Sign in to comment.