Rumor Detection
This dataset, comprising 103,806 text entries, is a comprehensive resource for rumor detection on social media, constructed by merging benchmark collections including PHEME, LIAR Fake News, Twitter15, Twitter16, and ISOT Fake News. It features a binary classification schema (47% rumor, 53% non-rumor) and integrates original and adversarially augmented samples to enhance model robustness.
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This database is provided for the Fake News Detection task. In addition to being used in other tasks of detecting fake news, it can be specifically used to detect fake news using the Natural Language Inference (NLI).
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