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Compromised accounts on social networks are regular user accounts that havebeen taken over by an entity with malicious intent. Since the adversaryexploits the already established trust of a compromised account, it is crucialto detect these accounts to limit the damage they can cause. We propose a novelgeneral framework for semantic analysis of text messages coming out from anaccount to detect compromised accounts. Our framework is built on theobservation that normal users will use language that is measurably differentfrom the language that an adversary would use when the account is compromised.We propose to use the difference of language models of users and adversaries todefine novel interpretable semantic features for measuring semantic incoherencein a message stream. We study the effectiveness of the proposed semanticfeatures using a Twitter data set. Evaluation results show that the proposedframework is effective for discovering compromised accounts on social networksand a KL-divergence-based language model feature works best.

Semantic Text Analysis for Detection of Compromised Accounts on Social Networks