  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.
