Emerging event detection in social networks with location sensitivity

Sayan Unankard, Xue Li, Mohamed A. Sharaf

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)

Abstract

With the increasing number of real-world events that are originated and discussed over social networks, event detection is becoming a compelling research issue. However, the traditional approaches to event detection on large text streams are not designed to deal with a large number of short and noisy messages. This paper proposes an approach for the early detection of emerging hotspot events in social networks with location sensitivity. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the emerging events. We evaluate our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively detect emerging events with respect to user locations that have different granularities.

Original languageEnglish
Pages (from-to)1393-1417
Number of pages25
JournalWorld Wide Web
Volume18
Issue number5
DOIs
Publication statusPublished - Sep 22 2015
Externally publishedYes

Keywords

  • Conceptual similarity
  • Emerging event detection
  • Location-based social networks
  • Short text clustering
  • Synonym expansion

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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