Event detection over twitter social media streams

In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis mana...

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Published in:	The VLDB journal Vol. 23; no. 3; pp. 381 - 400
Main Authors:	Zhou, Xiangmin, Chen, Lei
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2014 Springer
Subjects:	Applied sciences Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Database Management Exact sciences and technology Information retrieval. Graph Information systems. Data bases Memory organisation. Data processing Regular Paper Software Theoretical computing Graphical model Location-time constrained topic Social streams Social event detection Variable dimensional extendible hash Crisis management Mobile phone Information integration Event detection Similarity Instant messaging Probability distribution Efficiency Localization Target detection Monitoring Data analysis Duplication Decision making Inference Social network Real time Reactive system Graph method Web site
ISSN:	1066-8888, 0949-877X
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Abstract	In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.
AbstractList	In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.
Author	Zhou, Xiangmin Chen, Lei
Author_xml	– sequence: 1 givenname: Xiangmin surname: Zhou fullname: Zhou, Xiangmin email: xiangminemilyzhou@gmail.com, xiangmin.zhou@csiro.au organization: ICT Center, CSIRO – sequence: 2 givenname: Lei surname: Chen fullname: Chen, Lei organization: Department of Computer Science and Engineering, Hong Kong University of Science and Technology
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Keywords	Graphical model Location-time constrained topic Social streams Social event detection Variable dimensional extendible hash Crisis management Mobile phone Information integration Event detection Similarity Instant messaging Probability distribution Efficiency Localization Target detection Monitoring Data analysis Duplication Decision making Inference Social network Real time Reactive system Graph method Web site
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SubjectTerms	Applied sciences Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Database Management Exact sciences and technology Information retrieval. Graph Information systems. Data bases Memory organisation. Data processing Regular Paper Software Theoretical computing
Title	Event detection over twitter social media streams
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