Event detection in online social network: Methodologies, state-of-art, and evolution

Hu, X; Ma, W; Chen, C; Wen, S; Zhang, J; Xiang, Y; Fei, G

Event detection in online social network: Methodologies, state-of-art, and evolution

Hu, X Ma, W Chen, C Wen, S Zhang, J Xiang, Y Fei, G

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Computer Science Review, 2022, 46, pp. 100500
Issue Date:: 2022-11

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Field	Value	Language
dc.contributor.author	Hu, X
dc.contributor.author	Ma, W
dc.contributor.author	Chen, C
dc.contributor.author	Wen, S
dc.contributor.author	Zhang, J
dc.contributor.author	Xiang, Y
dc.contributor.author	Fei, G
dc.date.accessioned	2023-04-18T03:14:44Z
dc.date.available	2023-04-18T03:14:44Z
dc.date.issued	2022-11
dc.identifier.citation	Computer Science Review, 2022, 46, pp. 100500
dc.identifier.issn	1574-0137
dc.identifier.uri	http://hdl.handle.net/10453/169906
dc.description.abstract	Online social network such as Twitter, Facebook and Instagram are increasingly becoming the go-to medium for users to acquire information and discuss what is happening globally. Understanding real-time conversations with masses on social media platforms can provide rich insights into events, provided that there is a way to detect and characterise events. To this end, in the past twenty years, many researchers have been developing event detection methods based on the data collected from various social media platforms. The developed methods for discovering events are generally modular in design and novel in scale and speed. To review the research in this field, we line up existing works for event detection in online social networks and organise them to provide a comprehensive and in-depth survey. This survey comprises three major parts: research methodologies, the review of state-of-the-art literature and the evolution of significant challenges. Each part is supposed to attract readers with different motivations and expectations on the ‘things’ delivered in this survey. For example, the methodologies provide the life-cycle to design new event detection models, from data collection to model evaluations. A timeline and a taxonomy of existing methods are also introduced to elaborate the development of various technologies under the umbrella of event detection. These two parts benefit those with a background in event detection and want to commit a deep exploration of existing models such as discussing their pros and cons alike. The third part shows the development of the major open issues in this field. It also indicates the milestones of each challenge in terms of typical models. Our survey can contribute to the community by highlighting possible new problem statements and opening new research directions.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Computer Science Review
dc.relation.isbasedon	10.1016/j.cosrev.2022.100500
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Computation Theory & Mathematics
dc.title	Event detection in online social network: Methodologies, state-of-art, and evolution
dc.type	Journal Article
utslib.citation.volume	46
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T03:14:43Z
pubs.publication-status	Published
pubs.volume	46

Abstract:

Online social network such as Twitter, Facebook and Instagram are increasingly becoming the go-to medium for users to acquire information and discuss what is happening globally. Understanding real-time conversations with masses on social media platforms can provide rich insights into events, provided that there is a way to detect and characterise events. To this end, in the past twenty years, many researchers have been developing event detection methods based on the data collected from various social media platforms. The developed methods for discovering events are generally modular in design and novel in scale and speed. To review the research in this field, we line up existing works for event detection in online social networks and organise them to provide a comprehensive and in-depth survey. This survey comprises three major parts: research methodologies, the review of state-of-the-art literature and the evolution of significant challenges. Each part is supposed to attract readers with different motivations and expectations on the ‘things’ delivered in this survey. For example, the methodologies provide the life-cycle to design new event detection models, from data collection to model evaluations. A timeline and a taxonomy of existing methods are also introduced to elaborate the development of various technologies under the umbrella of event detection. These two parts benefit those with a background in event detection and want to commit a deep exploration of existing models such as discussing their pros and cons alike. The third part shows the development of the major open issues in this field. It also indicates the milestones of each challenge in terms of typical models. Our survey can contribute to the community by highlighting possible new problem statements and opening new research directions.

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http://hdl.handle.net/10453/169906