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You have been CAUTE! Early Detection of Compromised Accounts on Social Media

Detection of compromised social media accounts is an important problem as the compromised accounts can be exploited by hackers to spread false and misleading information. In particular, early detection of compromised accounts is essential to mitigating the damages caused by the hackers' posts, which may range from victim shaming to causing widespread public panic and civil unrest. This paper proposes CAUTE, a deep learning framework that simultaneously learns the feature embeddings of the users and their posts in order to identify which, if any, of their posts were written by a different person, i.e. a hacker. Using Twitter as an example of the social media platform, CAUTE learns a tweet-to-user encoder to infer the user features from tweet features and a user-to-tweet encoder to predict the tweet content from a combination of the user features and the tweet meta features. The residual errors of both encoders are then fed into a fully-connected neural network layer to detect whether a post was published by the specified user or by a hacker. Experimental results showed that the features learned by CAUTE are more informative than those generated by conventional representation learning methods. Additionally, CAUTE outperformed several state-of-the-art baseline algorithms in terms of their overall performance and can effectively detect compromised posts early without generating too many false alarms.