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

Rabia Bounaama, Mohammed El Amine Abderrahim
4/2/2023
cs.CL

No confident code implementation found for this paper. 5 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

RoBERTweet: A BERT Language Model for Romanian Tweets

85% match2023

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing

84% match2023

ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text

84% match2023

Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020

83% match2022

Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model

83% match2023

BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

83% match2026

Cite this paper

@article{bounaama2023classifying,
  title  = {Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models},
  author = {Rabia Bounaama and Mohammed El Amine Abderrahim},
  year   = {2023},
  eprint = {2304.00636},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.00636v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2304.00636.svg)](https://codeofpaper.com/paper/2304.00636)

Discussion