EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction

Social platforms have emerged as a crucial platform for disseminating and discussing information about real-life events, which offers an excellent opportunity for early detection of newsworthy events. However, most existing approaches for event detection solely exploit keyword burstiness or network structures to detect hot events. Thus, they often fail to identify emerging social events before reaching a trending state regarding the challenging nature of events and social data. Social data, e.g., tweets, is characterized by misspellings, incompleteness, ambiguity, and irregular language, as well as variation in aspects of opinions. Moreover, learning the evolving characteristics of the events utilizing limited contextual knowledge is almost infeasible for machine learning models. To address these problems, in this paper, we propose a framework that exploits the lexical, semantic, and contextual representations of streaming social data. In particular, we leverage contextual knowledge to detect semantically related tweets in their earliest emergence and enhance the quality of produced clusters. We next produce a cluster chains for each event to show the evolving variation of the event through time. We conducted extensive experiments to evaluate our framework, validating the effectiveness of the proposed framework in detecting and distinguishing social events.