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Communication Dans Un Congrès Année : 2023

Detection of COVID-19-Related Conpiracy Theories in Tweets using Transformer-Based Models and Node Embedding Techniques

Paolo Papotti
Raphaël Troncy

Résumé

With the amount of information shared on the internet increasing on a daily basis, we are prone to face more misinformation online. This is especially true on social media websites, where users have good amount of freedom to share their opinion. During the COVID-19 pandemic, numerous conspiracy theories were shared on Twitter. In this "FakeNews Detection" task, the goal is to detect COVID-19related conspiracy theories using tweet text and user interaction graph. We tackled this challenge using Transformer-based models (CT-BERT) and node embedding techniques (node2vec) with classification objective models. Our best model obtains a MCC score of 0.719 on the test data.
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Dates et versions

hal-03945796 , version 1 (18-01-2023)

Identifiants

  • HAL Id : hal-03945796 , version 1

Citer

Youri Peskine, Paolo Papotti, Raphaël Troncy. Detection of COVID-19-Related Conpiracy Theories in Tweets using Transformer-Based Models and Node Embedding Techniques. MediaEval 2022, Multimedia Evaluation Workshop, 12-13 January 2023, Bergen, Norway, Jan 2023, Bergen, Norway. ⟨hal-03945796⟩

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