Interaction graph, topical communities, and efficient local event detection from social streams

• Published in: Expert systems with applications 2023-12, Vol.232, p.120890, Article 120890
• Main Authors: Gupta, Shubham, Kundu, Suman
• Format: Article
• Language: English
• Subjects: Data stream mining; Event detection; Event extraction; Unsupervised learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.120890

Social networks have become an essential part of daily life, and hence every real-world activity finds its place in this virtual world. The present paper proposes a methodology to find localized micro-events from the social network stream. The method is named CommunityINDICATOR. A concept of ‘separation of concerns’ from the software design principle is incorporated in the methodology to reduce the execution time drastically from existing state-of-the-art methods of event detection. In order to reduce the execution time, the algorithm first generates an interaction graph from the social stream and applies community detection followed by a clustering algorithm onto it to detect micro-level events. Experiments have been conducted on Twitter data stream of 5 different cities on three different continents with the size of 2 million tweets. We have used well known quality metrics such as precision, recall, F1-score, accuracy, and execution time to compare performance with other state-of-the-art methodologies. The proposed CommunityINDICATOR provides up to 30% higher accuracy than EvenTweet and SEDTWik in similar execution times. An improvement of 11% to 51% and 17% to 57% in execution time is observed for the proposed algorithm in comparison with TwiiterNews and EventX, respectively, for different datasets. •An e2e event detection pipeline by introducing “separation of concerns” concept.•Achieving time benefit in existing unsupervised method by creating interaction graph.•Identifying very localized events with high inference speed and detection accuracy.•Experiments performed to check the effect of time and partition algorithm in pipeline.