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Analysis of psycholinguistic processes and topics in online autism communities

Nguyen, Thin, Phung, Dinh and Venkatesh, Svetha 2013, Analysis of psycholinguistic processes and topics in online autism communities, in ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/ICME.2013.6607615.

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Title Analysis of psycholinguistic processes and topics in online autism communities
Author(s) Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Multimedia and Expo. IEEE International Conference (14th : 2013 : San Jose, California)
Conference location San Jose, California
Conference dates 15-19 Jul. 2013
Title of proceedings ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Multimedia and Expo
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) autism
psycholinguistic
social media
Summary Current growth of individuals on the autism spectrum disorder (ASD) requires continuous support and care. With the popularity of social media, online communities of people affected by ASD emerge. This paper presents an analysis of these online communities through understanding aspects that differentiate such communities. In this paper, the aspects given are not expressed in terms of friendship, exchange of information, social support or recreation, but rather with regard to the topics and linguistic styles that people express in their on-line writing. Using data collected unobtrusively from LiveJournal, we analyze posts made by ten autism communities in conjunction with those made by a control group of standard communities. Significant differences have been found between autism and control communities when characterized by latent topics of discussion and psycholinguistic features. Latent topics are found to have greater predictive power than linguistic features when classifying blog posts as either autism or control community. This study suggests that data mining of online blogs has the potential to detect clinically meaningful data. It opens the door to possibilities including sentinel risk surveillance and harnessing the power in diverse large datasets.
ISBN 9781479900152
Language eng
DOI 10.1109/ICME.2013.6607615
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 920599 Specific Population Health (excl. Indigenous Health) not elsewhere classified
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2013
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057164

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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Created: Wed, 23 Oct 2013, 10:02:40 EST

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