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Finding dense subgraphs and events in social media

Abstract : Event detection in social media is the task of finding mentions of real-world events in collections of posts. The motivation behind our work is two-folded: first, finding events that are not covered by mainstream media and second, studying the interest that users show for certain types of events. In order to solve our problem, we start from a graph based characterization of the data in which nodes represent words and edges count word co-occurrences. Density is a very good measure of importance and cohesiveness in graphs. Taking into account the special properties of real-word networks, we can develop algorithms that efficiently solve hard problems. The contributions of this thesis are: devising efficient algorithms for computing different types of dense subgraphs in real-world graphs, presenting a novel dense subgraph definition and providing an efficient graph-based algorithm for event detection.
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Submitted on : Monday, May 30, 2022 - 5:14:12 PM
Last modification on : Tuesday, May 31, 2022 - 3:43:54 AM
Long-term archiving on: : Wednesday, August 31, 2022 - 7:21:30 PM


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  • HAL Id : tel-03682101, version 1


Oana Balalau. Finding dense subgraphs and events in social media. Social and Information Networks [cs.SI]. Télécom ParisTech, 2017. English. ⟨NNT : 2017ENST0020⟩. ⟨tel-03682101⟩



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