Papers by Yuki Kishi

1 papers
Fake News Detection Strategies under Dataset Bias: Using Large-scale Coarse-grained Labels (2026.eacl-srw)

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Challenge: Existing datasets differ substantially in content distributions and annotation policies, complicating fair evaluation and generalization assessment.
Approach: They quantitatively analyze dataset bias across multiple public fake news datasets with different annotation granularities, including article-level and publisher-level labels.
Outcome: The proposed approach improves detection performance under in-dataset and cross-data set evaluation settings.

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