Complex Network and Source Inspired COVID-19 Fake News Classification on Twitter

ABSTRACT

In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception. It puts a tremendous strain on society by damaging reputation, public trust, freedom of expression, journalism, justice, truth, and democracy. Therefore, it is of paramount importance to detect and contain unreliable information. Multiple techniques have been proposed to detect fake news propagation in tweets based on tweets content, propagation on the network of users, and the profile of the news generators. Generating human-like content allows deceiving content-based methods. Network-based methods rely on the complete graph to detect fake news, resulting in late detection. User profile-based techniques are effective for bots or fake accounts detection. However, they are not suited to detect fake news from original accounts. To deal with the shortcomings in existing methods, we introduce a source-based method focusing on the news propagators’community, including posters and re-tweeters to detect such contents. Propagators are connected using follower-following relations. A feature set combining the connectivity patterns of news propagators with their profile features is used in a machine learning framework to perform binary classification of tweets. Complex network measures and user profile features are also examined separately. We perform an extensive comparative analysis of the proposed methodology on a real-world COVID-19 dataset, exploiting various machine learning and deep learning models at the community and node levels. Results show that hybrid features perform better than network features and user features alone. Further optimization demonstrates that Ensemble’s boosting model CATBoost and deep learning model RNN are the most effective, with an AUC score of 98%. Furthermore, preliminary results show that the proposed solution can also handle fake news in the political and entertainment domain using a small training set. Author