Event detection in Twitter microblogging

Nikolaos D. Doulamis, Anastasios D. Doulamis, Panagiotis Kokkinos, Emmanouel (Manos) Varvarigos

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)

Abstract

The millions of tweets submitted daily overwhelm users who find it difficult to identify content of interest revealing the need for event detection algorithms in Twitter. Such algorithms are proposed in this paper covering both short (identifying what is currently happening) and long term periods (reviewing the most salient recently submitted events). For both scenarios, we propose fuzzy represented and timely evolved tweet-based theoretic information metrics to model Twitter dynamics. The Riemannian distance is also exploited with respect to words' signatures to minimize temporal effects due to submission delays. Events are detected through a multiassignment graph partitioning algorithm that: 1) optimally retains maximum coherence within a cluster and 2) while allowing a word to belong to several clusters (events). Experimental results on real-life data demonstrate that our approach outperforms other methods.

Original languageEnglish
Pages (from-to)2810-2824
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume46
Issue number12
DOIs
Publication statusPublished - 1 Dec 2016
Externally publishedYes

Keywords

  • Clustering
  • Document and text processing
  • Fuzzy representation
  • Pattern analysis
  • Tweet characterization

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