Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models
This addresses misinformation and public sentiment analysis during the COVID-19 pandemic, but is incremental as it applies existing methods to a specific dataset.
The paper tackled classifying COVID-19 tweets for fake news detection and sentiment analysis using BERT-based models, achieving accuracies of 0.93 for sentiment analysis and 0.90 for fake news detection.
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.