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Big Data Mining and Analytics 2024, 7(1): 217-243 https://doi.org/10.26599/BDMA.2023.9020021
Open Access | Issue | Published: 25 December 2023

A Survey on Event Tracking in Social Media Data Streams

Show Author's Information Zixuan Han1,  Leilei Shi1( ),  Lu Liu2( ),  Liang Jiang3,  Jiawei Fang1,  Fanyuan Lin1,  Jinjuan Zhang1,  John Panneerselvam2,  Nick Antonopoulos4
School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, China
School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China
University Executive Office, Edinburgh Napier University, Edinburgh, EH11 4BN, UK
Keywords:
social networks, Event Detection (ED), event propagation, event evolution
Cite this article:
Han Z, Shi L, Liu L, et al. A Survey on Event Tracking in Social Media Data Streams. Big Data Mining and Analytics, 2024, 7(1): 217-243. https://doi.org/10.26599/BDMA.2023.9020021
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Abstract Full text About this article

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.


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About this article

A Survey on Event Tracking in Social Media Data Streams

Show Author's information Zixuan Han1, Leilei Shi1( ), Lu Liu2( ), Liang Jiang3, Jiawei Fang1, Fanyuan Lin1, Jinjuan Zhang1, John Panneerselvam2, Nick Antonopoulos4
School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, China
School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China
University Executive Office, Edinburgh Napier University, Edinburgh, EH11 4BN, UK

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.

Keywords: social networks, Event Detection (ED), event propagation, event evolution

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Received: 03 November 2022
Revised: 24 April 2023
Accepted: 11 August 2023
Published: 25 December 2023
Issue date: March 2024

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© The author(s) 2023.

Acknowledgements

Acknowledgment

The work was supported by the National Natural Science Foundation of China (No. 62302199), the China Postdoctoral Science Foundation (No. 2023M731368), the Natural Science Foundation of the Jiangsu Higher Education Institutions (No. 22KJB520016), the Jiangsu University Innovative Research Project (No. KYCX22_3671), the Youth Foundation Project of Humanities and Social Sciences of Ministry of Education in China (No. 22YJC870007), the Jiangsu University Undergraduate Student English Teaching Excellence Program, and the Ministry of Education's Industry-Education Cooperation Collaborative Education Project (No. 202102306005).

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