Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 2 Apr 2023.
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References · 9
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2019How to Fine-Tune BERT for Text Classification?cited by 411
- 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
- 2020Revisiting Few-sample BERT Fine-tuningcited by 238
- 2020KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Mediacited by 84
- 2022Arabic Fake News Detection Based on Deep Contextualized Embedding Modelscited by 1
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