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arXiv · 2304.00636

Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models

Rabia Bounaama·Mohammed El Amine Abderrahim
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Abstract

The present paper is about the participation of our team "techno" on CERIST'22 shared tasks. We used an available dataset "task1.c" related to covid-19 pandemic. It comprises 4128 tweets for sentiment analysis task and 8661 tweets for fake news detection task. We used natural language processing tools with the combination of the most renowned pre-trained language models BERT (Bidirectional Encoder Representations from Transformers). The results shows the efficacy of pre-trained language models as we attained an accuracy of 0.93 for the sentiment analysis task and 0.90 for the fake news detection task.

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Rabia Bounaama, Mohammed El Amine Abderrahim. 2023-04-02. Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models. https://arxiv.org/abs/2304.00636

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