Paper ID: 2304.00636

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

Rabia Bounaama, Mohammed El Amine Abderrahim

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.

Submitted: Apr 2, 2023