Time Profiles for Identifying Users in Online Environments

摘要: Many people who discuss sensitive or private issues on web forums and other social media services are using pseudonyms aliases in order to not reveal their true identity, while usual accounts when posting messages nonsensitive issues. Previous research has shown that if those individuals post large amounts of messages, stylometric techniques can be used identify the author based characteristics textual content. In this paper we show how an author's identity unmasked a similar way various time features, such as period day week user's posts have been published. This is demonstrated supervised machine learning (i.e., identification) experiments, well unsupervised alias matching (similarity detection) experiments.