Affective, linguistic and topic patterns in online autism communities Nguyen,T, Duong,T, Phung,D and Venkatesh,S 2014, Affective, linguistic and topic patterns in online autism communities. In Benatallah,B, Bestavros,A, Manolopoulos,Y, Vakali,A and Zhang,Y (ed), Web information systems engineering -- WISE 2014 : 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, proceedings, Springer, Berlin, Germany, pp.474-488, doi: 10.1007/978-3-319-11746-1_35. Attached Files Name Description MIMEType Size Downloads Title Affective, linguistic and topic patterns in online autism communities Author(s) Nguyen,T orcid.org/0000-0003-3467-8963 Duong,T Phung,D orcid.org/0000-0002-9977-8247 Venkatesh,S orcid.org/0000-0001-8675-6631 Title of book Web information systems engineering -- WISE 2014 : 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, proceedings Editor(s) Benatallah,B Bestavros,A Manolopoulos,Y Vakali,A Zhang,Y Publication date 2014 Series Lecture Notes in Computer Science Chapter number 35 Total chapters 40 Start page 474 End page 488 Total pages 15 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Affective computing Mental health Web mining Weblog Science & Technology Technology Computer Science, Information Systems Computer Science, Theory & Methods Computer Science SPECTRUM DISORDERS CHILDREN DEPRESSION STRESS MOOD INTERVENTION ASSOCIATIONS SYMPTOMS FACEBOOK STUDENTS Summary Online communities offer a platform to support and discuss health issues. They provide a more accessible way to bring people of the same concerns or interests. This paper aims to study the characteristics of online autism communities (called Clinical) in comparison with other online communities (called Control) using data from 110 Live Journal weblog communities. Using machine learning techniques, we comprehensively analyze these online autism communities. We study three key aspects expressed in the blog posts made by members of the communities: sentiment, topics and language style. Sentiment analysis shows that the sentiment of the clinical group has lower valence, indicative of poorer moods than people in control. Topics and language styles are shown to be good predictors of autism posts. The result shows the potential of social media in medical studies for a broad range of purposes such as screening, monitoring and subsequently providing supports for online communities of individuals with special needs. ISBN 9783319117454 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-11746-1_35 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter ERA Research output type B Book chapter Grant ID ARC LP140100240 Copyright notice ©2014, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30072249 Document type: Book Chapter Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 14 Apr 2015, 11:36:36 EST Mon, 20 Apr 2015, 11:27:35 EST Tue, 21 Apr 2015, 05:00:49 EST Tue, 21 Apr 2015, 10:02:57 EST Wed, 22 Apr 2015, 08:17:11 EST Fri, 24 Apr 2015, 06:10:17 EST Fri, 24 Apr 2015, 16:25:35 EST Fri, 24 Apr 2015, 16:26:18 EST Fri, 05 Oct 2018, 11:36:25 EST Thu, 25 Oct 2018, 10:10:02 EST Thu, 25 Oct 2018, 10:10:49 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 376 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Tue, 21 Apr 2015, 10:02:57 EST

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