We detail a deep learning approach based on the transformer architecture for performing fake news detection. The proposed approach is composed of a deep learning network which receives as input the claim to be verified, a series of predictions made by other models, and supporting evidence in the form of ranked passages. We validate our approach participating as the Brisbane–Udine–Melbourne (BUM) Team in the CLEF2022-CheckThat! Lab (Task 3: Fake News Detection), where we achieve an F1-score of 0.275, ranking 10th out of 25 participants.1
BUM at CheckThat! 2022: A Composite Deep Learning Approach to Fake News Detection using Evidence Retrieval
La Barbera D.
;Roitero K.;Demartini G.;Mizzaro S.
2022-01-01
Abstract
We detail a deep learning approach based on the transformer architecture for performing fake news detection. The proposed approach is composed of a deep learning network which receives as input the claim to be verified, a series of predictions made by other models, and supporting evidence in the form of ranked passages. We validate our approach participating as the Brisbane–Udine–Melbourne (BUM) Team in the CLEF2022-CheckThat! Lab (Task 3: Fake News Detection), where we achieve an F1-score of 0.275, ranking 10th out of 25 participants.1File in questo prodotto:
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