Identifying Compromised Accounts on Social Media Using Statistical Text Analysis

Compromised social media accounts are legitimate user accounts that have been hijacked by a malicious party and can cause various kinds of damage, which makes the detection of these accounts crucial. In this work we propose a novel general framework for discovering compromised accounts by utilizing statistical text analysis. The framework is built on the observation that users will use language that is measurably different from the language that an attacker would use, when the account is compromised. We use the framework to develop specific algorithms based on language modeling and use the similarity of language models of users and attackers as features in a supervised learning setup to identify compromised accounts. Evaluation results on a large Twitter corpus of over 129 million tweets show promising results of the proposed approach.