DhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream

Guo, J; Gong, Z; Cao, L

DhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream

Guo, J Gong, Z Cao, L

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology, 2021, 12, (5)
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Guo, J
dc.contributor.author	Gong, Z
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2022-04-15T21:50:24Z
dc.date.available	2022-04-15T21:50:24Z
dc.date.issued	2021-10-01
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology, 2021, 12, (5)
dc.identifier.issn	2157-6904
dc.identifier.issn	2157-6912
dc.identifier.uri	http://hdl.handle.net/10453/156293
dc.description.abstract	The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes dynamic and hierarchical Categorization Modeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation	http://purl.org/au-research/grants/arc/DP190101079
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology
dc.relation.isbasedon	10.1145/3470888
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.title	DhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-15T21:50:22Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	5

Abstract:

The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes dynamic and hierarchical Categorization Modeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.

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http://hdl.handle.net/10453/156293