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Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning

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Abstract

We present in this paper our participation to the task of fake news conspiracy theories detection from tweets. We rely on a variant of BERT-based classification approach to devise a first classification method for the three different tasks. Moreover, we propose a multitask learning approach to perform the three different tasks at once. Finally, we developed a prompt-based approach to generate classifications thanks to a TinyBERT pre-trained model. Our experimental results show the multitask model to be the best on the three tasks.
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Dates and versions

hal-03482254 , version 1 (15-12-2021)

Identifiers

  • HAL Id : hal-03482254 , version 1

Cite

Cheikh Brahim El Vaigh, Thomas Girault, Cyrielle Mallart, Duc Hau Nguyen. Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning. MediaEval 2021 - MediaEval Multimedia Evaluation benchmark. Workshop, Dec 2021, Online, Netherlands. pp.1-3. ⟨hal-03482254⟩
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