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Affective, linguistic and topic patterns in online autism communities
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posted on 2014-01-01, 00:00 authored by Thin NguyenThin Nguyen, Thi Duong, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshOnline 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.
History
Title of book
Web information systems engineering -- WISE 2014 : 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, proceedingsVolume
8787Series
Lecture Notes in Computer ScienceChapter number
35Pagination
474 - 488Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319117454Language
engPublication classification
B1 Book chapter; B Book chapterCopyright notice
2014, SpringerExtent
40Editor/Contributor(s)
B Benatallah, A Bestavros, Y Manolopoulos, A Vakali, Y ZhangUsage metrics
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