Entity-level Event Impact Analytics

Abstract : Our society has been rapidly growing its presence on the Web, as a consequence we are digitizing a large collection of our daily happenings. In this scenario, the Web receives virtual occurrences of various events corresponding to their real world occurrences from all around the world. Scale of these events can vary from locally relevant ones up to those that receive global attention. News and social media of current times provide all essential means to reach almost a global diffusion. This big data of complex societal events provide a platform to many research opportunities for analyzing and gaining insights into the state of our society.In this thesis, we investigate a variety of social event impact analytics tasks. Specifically, we address three facets in the context of events and the Web, namely, diffusion of events in foreign languages communities, automated classification of Web contents, and news virality assessment and visualization. We hypothesize that the named entities associated with an event or a Web content carry valuable semantic information, which can be exploited to build accurate prediction models. We have shown with the help of multiple studies that raising Web contents to the entity-level captures their core essence, and thus, provides a variety of benefits in achieving better performance in diverse tasks. We report novel findings over disparate tasks in an attempt to fulfill our overall goal on societal event impact analytics.
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. Govind. Entity-level Event Impact Analytics. Computation and Language [cs.CL]. Normandie Université, 2018. English. ⟨NNT : 2018NORMC260⟩. ⟨tel-02158767⟩

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