A Survey on Event Tracking in Social Media Data Streams
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 optimiz...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020021 |
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| _version_ | 1832544198797033472 |
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| author | Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos |
| author_facet | Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos |
| author_sort | Zixuan Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5df577c063bc46ef97d6bd3e5d56c47c |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-5df577c063bc46ef97d6bd3e5d56c47c2025-02-03T10:49:41ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-03-017121724310.26599/BDMA.2023.9020021A Survey on Event Tracking in Social Media Data StreamsZixuan Han0Leilei Shi1Lu Liu2Liang Jiang3Jiawei Fang4Fanyuan Lin5Jinjuan Zhang6John Panneerselvam7Nick Antonopoulos8School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UKOcean College, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UKUniversity Executive Office, Edinburgh Napier University, Edinburgh, EH11 4BN, UKSocial 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020021event detection (ed)event propagationevent evolutionsocial networks |
| spellingShingle | Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos A Survey on Event Tracking in Social Media Data Streams Big Data Mining and Analytics event detection (ed) event propagation event evolution social networks |
| title | A Survey on Event Tracking in Social Media Data Streams |
| title_full | A Survey on Event Tracking in Social Media Data Streams |
| title_fullStr | A Survey on Event Tracking in Social Media Data Streams |
| title_full_unstemmed | A Survey on Event Tracking in Social Media Data Streams |
| title_short | A Survey on Event Tracking in Social Media Data Streams |
| title_sort | survey on event tracking in social media data streams |
| topic | event detection (ed) event propagation event evolution social networks |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020021 |
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