CADET: A Multi-View Learning Framework for Compromised Account Detection on Twitter

Social media has become a valuable tool for hackers to disseminate misleading content through compromised accounts. Detecting compromised accounts, however, is challenging due to the noisy nature of social media posts and the difficulty in acquiring sufficient labeled data that can effectively capture a wide variety of compromised tweets from different types of hackers (spammers, vandals, cybercriminals, revenge hackers, etc). To address these challenges, this proposal presents a multi-view learning framework that employs nonlinear autoencoders to learn the feature embedding from multiple views, such as the tweets' content, source, location, and timing information and then projects the embedded features into a common lower-rank feature representation. Suspicious user accounts are detected based on their reconstruction errors in the shared subspace. Our empirical results show the superiority of CADET compared to several existing representative approaches when applied to a real-world Twitter dataset.