PUB - Publikationen an der Universität Bielefeld

Events play a prominent role in our lives, such that many social media documents describe or are related to some event. Organizing social media documents with respect to events thus seems a promising approach to better manage and organize the ever-increasing amount of content in social media applications. A challenge is to automatize this process so that incoming documents can be assigned to their corresponding event without any user intervention. We present a system that is able to classify a stream of social media data into a growing and evolving set of events. By doing this, we successfully address two key problems that arise in this context: i) scaling to the data sizes and rates encountered in social media applications, and ii) tackling the new event detection problem, i.e. the problem of determining whether an incoming data item belongs to a new or a known event. We successfully address these problems by i) including a candidate retrieval step that retrieves a set of event candidates that the incoming data point is likely to belong to and ii) by including a function trained using machine learning techniques to determine whether the incoming data item belongs to the top scoring candidate or rather to a new event. We show that our system addresses the above mentioned challenging issues successfully and that it outperforms other state-of-the-art approaches in terms of quality and scalability.