Fake News Analysis and Graph Classification on a COVID-19 Twitter Dataset

Abstract

Earlier researches have showed that the spread of fake news through social media can have a huge impact to society and also to individuals in an extremely negative way. In this work we aim to study the spread of fake news compared to real news in a social network. We do that by performing classical social network analysis to discover various characteristics, and formulate the problem as a binary classification, where we have graphs modeling the spread of fake and real news. For our experiments we rely on how news are propagated through a popular social media services such as Twitter during the pandemic caused by the COVID-19 virus. In the past, several other approaches classify news as fake or real by deploying various graph embedding techniques and deep learning techniques. In this project we focus on developing a dataset that contains tweets specific to COVID-19 by performing initially text mining on the content of the tweet. Further, we create graphs of the fake and real news along with their retweets and followers and work on the graphs. We perform social network analysis and compare their characteristics. We study the propagation of fake and real news among users using community detection algorithms on the graphs. Finally, we create a model by deploying the Weisfeiler Lehman graph kernel for graph classification on our labeled dataset. The model is able to predict whether a new article is real or fake based on how the corresponding graph of the retweets and followers are connected.