Real-time event detection from the Twitter data stream using the TwitterNews+ Framework. Issue 3 (May 2019)

• Record Type: Journal Article
• Authors: Hasan, Mahmud; Orgun, Mehmet A.; Schwitter, Rolf
• Is Part Of: Information processing & management. Volume 56:Issue 3(2019:May)
• Page Start: 1146
• Page End: 1165
• Publication Date: 2019-05
• Subjects: Event detection -- Incremental clustering -- Social media -- Microblog -- Twitter -- Parameter sensitivity analysis; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2018.03.001
• Languages: English
• ISSNs: 0306-4573

Abstract

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.