Tackling COVID-19 Infodemic using Deep Learning

TL;DR

This paper explores various machine learning and deep learning methods to detect and classify COVID-19 related fake news on online media, aiming to combat misinformation during the pandemic.

Contribution

It compares traditional classifiers and deep learning models using TF-IDF and GloVe features for COVID-19 fake news detection.

Findings

Deep learning models outperform traditional classifiers.

GloVe embeddings yield better results than TF-IDF.

Ensemble RMDL achieves highest accuracy.

Abstract

Humanity is battling one of the most deleterious virus in modern history, the COVID-19 pandemic, but along with the pandemic there's an infodemic permeating the pupil and society with misinformation which exacerbates the current malady. We try to detect and classify fake news on online media to detect fake information relating to COVID-19 and coronavirus. The dataset contained fake posts, articles and news gathered from fact checking websites like politifact whereas real tweets were taken from verified twitter handles. We incorporated multiple conventional classification techniques like Naive Bayes, KNN, Gradient Boost and Random Forest along with Deep learning approaches, specifically CNN, RNN, DNN and the ensemble model RMDL. We analyzed these approaches with two feature extraction techniques, TF-IDF and GloVe Word Embeddings which would provide deeper insights into the dataset containing COVID-19 info on online media.