We present a framework for high-level automatic event detection and user classification in a social network context based on a novel temporal extension of relational algebra, which improves and extends our earlier work in the video surveillance context. By means of intuitive and interactive graphical user interfaces, a user is able to gain insights into the inner workings of the system as well as create new event models and user categories on the fly and track their processing through the system in both offline and online modes. Compared to an earlier version, we extended our relational algebra framework with operators suited for processing data from a social network context. As a proof-of-concept we have predefined events and user categories, such as spamming and fake users, on both a synthetic and a real data set containing data related to the interactions of users with Facebook over a 2-year period.
Event query languages High-level event detection Intervals Social network analysis Behavior identification in OSNs
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