Time Profiles for Identifying Users in Online Environments

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