What’s happening around the world? A survey and framework on event detection techniques on Twitter

Zafar SAEED, Rabeeh Ayaz ABBASI, Onaiza MAQBOOL, Abida SADAF, Imran RAZZAK, Ali DAUD, Naif Radi ALJOHANI, Guandong XU

Research output: Contribution to journalArticlespeer-review

67 Citations (Scopus)

Abstract

In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter. Copyright © 2019 Springer Nature B.V.

Original languageEnglish
Pages (from-to)279-312
JournalJournal of Grid Computing
Volume17
Early online dateMay 2019
DOIs
Publication statusPublished - Jun 2019

Citation

Saeed, Z., Abbasi, R. A., Maqbool, O., Sadaf, A., Razzak, I., Daud, A., Aljohani, N. R., & Xu, G. (2019). What’s happening around the world? A survey and framework on event detection techniques on Twitter. Journal of Grid Computing, 17, 279-312. https://doi.org/10.1007/s10723-019-09482-2

Keywords

  • Events
  • Event detection
  • Twitter
  • Social media
  • Machine learning
  • Survey
  • Taxonomy
  • Framework

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