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), , Springer, Berlin, Germany, pp.474-488, doi: 10.1007/978-3-319-11746-1_35.

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Title Affective, linguistic and topic patterns in online autism communities
Author(s) Nguyen,TORCID iD for Nguyen,T
Phung,DORCID iD for Phung,D
Venkatesh,SORCID iD for Venkatesh,S
Editor(s) Benatallah,B
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
Science & Technology
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
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
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
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