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Conference Paper: Collective Intelligence for Suicide Surveillance in Web Forums

TitleCollective Intelligence for Suicide Surveillance in Web Forums
Authors
KeywordsAffect analysis
Collective intelligence
Machine learning
Opinion summarization
Suicide surveillance
Text analysis
Web forums
Issue Date2013
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2013), Beijing, China, 3-5 August 2013. In Lecture Notes in Computer Science, 2013, v. 8039, p. 29-37 How to Cite?
AbstractInternet users may see and comment on suicide expressions in the cyberspace that are not identified by helping professionals. The communication between the two parties is not well facilitated. In light of this situation, we present a system using collective intelligence from Internet users to efficiently and effectively identify suicidal people in order to provide timely intervention and promote better public health. We describe the system architecture involving information retrieval, affect analysis, and opinion summarization technique. The collective intelligence approach incorporates machine learning techniques and Internet users' contributions to facilitate the automated identification of suicide expressions. The system will be examined and evaluated in lab settings and suicide prevention organizations. © 2013 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/185119
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorLi, TMHen_US
dc.contributor.authorNg, BCMen_US
dc.contributor.authorChau, MCLen_US
dc.contributor.authorWong, PWCen_US
dc.contributor.authorYip, PSFen_US
dc.date.accessioned2013-07-15T10:31:51Z-
dc.date.available2013-07-15T10:31:51Z-
dc.date.issued2013en_US
dc.identifier.citationPacific Asia Workshop on Intelligence and Security Informatics (PAISI 2013), Beijing, China, 3-5 August 2013. In Lecture Notes in Computer Science, 2013, v. 8039, p. 29-37en_US
dc.identifier.isbn978-364239692-2-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/185119-
dc.description.abstractInternet users may see and comment on suicide expressions in the cyberspace that are not identified by helping professionals. The communication between the two parties is not well facilitated. In light of this situation, we present a system using collective intelligence from Internet users to efficiently and effectively identify suicidal people in order to provide timely intervention and promote better public health. We describe the system architecture involving information retrieval, affect analysis, and opinion summarization technique. The collective intelligence approach incorporates machine learning techniques and Internet users' contributions to facilitate the automated identification of suicide expressions. The system will be examined and evaluated in lab settings and suicide prevention organizations. © 2013 Springer-Verlag.-
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectAffect analysis-
dc.subjectCollective intelligence-
dc.subjectMachine learning-
dc.subjectOpinion summarization-
dc.subjectSuicide surveillance-
dc.subjectText analysis-
dc.subjectWeb forums-
dc.titleCollective Intelligence for Suicide Surveillance in Web Forumsen_US
dc.typeConference_Paperen_US
dc.identifier.emailNg, BCM: bencmng@hku.hken_US
dc.identifier.emailChau, MCL: mchau@business.hku.hken_US
dc.identifier.emailWong, PWC: paulw@hku.hken_US
dc.identifier.emailYip, PSF: sfpyip@hku.hken_US
dc.identifier.authorityChau, MCL=rp01051en_US
dc.identifier.authorityWong, PWC=rp00591en_US
dc.identifier.authorityYip, PSF=rp00596en_US
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-39693-9_4-
dc.identifier.scopuseid_2-s2.0-84883204286-
dc.identifier.hkuros216016en_US
dc.identifier.volume8039-
dc.identifier.spage29-
dc.identifier.epage37-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 160216 - amend-

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