Tackling COVID-19 Infodemic using Deep Learning
This addresses the COVID-19 infodemic problem for public health authorities and social media platforms, though it is incremental as it applies existing methods to a new domain.
The researchers tackled the problem of COVID-19 misinformation by developing a system to detect and classify fake news using a dataset from fact-checking websites and verified Twitter handles. They compared multiple conventional classification techniques and deep learning approaches with TF-IDF and GloVe feature extraction, finding that ensemble models performed best with accuracy improvements of 5-10% over baseline methods.
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.