Real-time event detection from the Twitter data stream using the TwitterNews+ framework

Mahmud Hasan, Mehmet A. Orgun*, Rolf Schwitter

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract

Detecting events in real-time from the Twitter data stream has gained substantial attention in recent years from researchers around the world. Different event detection approaches have been proposed as a result of these research efforts. One of the major challenges faced in this context is the high computational cost associated with event detection in real-time. We propose, TwitterNews+, an event detection system that incorporates specialized inverted indices and an incremental clustering approach to provide a low computational cost solution to detect both major and minor newsworthy events in real-time from the Twitter data stream. In addition, we conduct an extensive parameter sensitivity analysis to fine-tune the parameters used in TwitterNews+ to achieve the best performance. Finally, we evaluate the effectiveness of our system using a publicly available corpus as a benchmark dataset. The results of the evaluation show a significant improvement in terms of recall and precision over five state-of-the-art baselines we have used.

Original languageEnglish
Pages (from-to)1146-1165
Number of pages20
JournalInformation Processing and Management
Volume56
Issue number3
Early online date13 Mar 2018
DOIs
Publication statusPublished - May 2019

Keywords

  • Event detection
  • Incremental clustering
  • Microblog
  • Parameter sensitivity analysis
  • Social media
  • Twitter

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