Title: State-of-the-art approaches for event detection over Twitter stream: a survey

Authors: Jagrati Singh; Anil Kumar Singh

Addresses: Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India ' Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India

Abstract: In the present time, social network applications like Twitter, Facebook and YouTube have evolved as a popular way of information sharing for general users. On these platforms, valuable information appears as breaking hot news, trending topics, public opinion, and so on. Twitter is the most popular microblogging service that generates huge volumes of data with high velocity and variety (i.e., images, text and video). Due to the growth of discussed real-world events over Twitter, the event detection problem is becoming an interesting and challenging issue. Event detection is the practice of applying natural language processing and text analysis techniques to identify and extract event information from text. This survey paper explores important research works for event detection using Twitter data. We classify approaches according to feature modelling methods: vector space model, statistical model and graph model. We highlight research challenges, issues, and the limitation of existing approaches to find the research gaps for future directions.

Keywords: Twitter stream; clustering; data sharing; supervised technique; unsupervised technique; semantic correlation; keyword co-occurrence; topic modelling.

DOI: 10.1504/IJBIDM.2023.134309

International Journal of Business Intelligence and Data Mining, 2023 Vol.23 No.4, pp.325 - 374

Received: 04 May 2021
Accepted: 03 Apr 2022
Published online: 18 Oct 2023 *

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