A Survey on Event Tracking in Social Media Data Streams

Han, Zixuan; Shi, Leilei; Liu, Lu; Jiang, Liang; Fang, Jiawei; Lin, Fanyuan; Zhang, Jinjuan; Panneerselvam, John; Antonopoulos, Nick

Abstract

Social networks are inevitable parts of our daily life, where an unprecedented amount of complex data corresponding to a diverse range of applications are generated. As such, it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks. Event tracking in social networks finds various applications, such as network security and societal governance, which involves analyzing data generated by user groups on social networks in real time. Moreover, as deep learning techniques continue to advance and make important breakthroughs in various fields, researchers are using this technology to progressively optimize the effectiveness of Event Detection (ED) and tracking algorithms. In this regard, this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks. We introduce mainstream event tracking methods, which involve three primary technical steps: ED, event propagation, and event evolution. Finally, we introduce benchmark datasets and evaluation metrics for ED and tracking, which allow comparative analysis on the performance of mainstream methods. Finally, we present a comprehensive analysis of the main research findings and existing limitations in this field, as well as future research prospects and challenges.

Citation

Han, Z., Shi, L., Liu, L., Jiang, L., Fang, J., Lin, F., Zhang, J., Panneerselvam, J., & Antonopoulos, N. (2024). A Survey on Event Tracking in Social Media Data Streams. Big Data Mining and Analytics, 7(1), 217-243. https://doi.org/10.26599/bdma.2023.9020021

• Journal Article Type: Article
• Online Publication Date: Dec 25, 2023
• Publication Date: 2024-03
• Deposit Date: Aug 8, 2024
• Publicly Available Date: Aug 8, 2024
• Journal: Big Data Mining and Analytics
• Print ISSN: 2096-0654
• Electronic ISSN: 2097-406X
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Pages: 217-243
• DOI: https://doi.org/10.26599/bdma.2023.9020021