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Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis
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posted on 2014-01-01, 00:00 authored by B Dao, Thin NguyenThin Nguyen, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshDepression afflicts one in four people during their lives. Several studies have shown that for the isolated and mentally ill, the Web and social media provide effective platforms for supports and treatments as well as to acquire scientific, clinical understanding of this mental condition. More and more individuals affected by depression join online communities to seek for information, express themselves, share their concerns and look for supports [12]. For the first time, we collect and study a large online depression community of more than 12,000 active members from Live Journal. We examine the effect of mood, social connectivity and age on the online messages authored by members in an online depression community. The posts are considered in two aspects: what is written (topic) and how it is written (language style). We use statistical and machine learning methods to discriminate the posts made by bloggers in low versus high valence mood, in different age categories and in different degrees of social connectivity. Using statistical tests, language styles are found to be significantly different between low and high valence cohorts, whilst topics are significantly different between people whose different degrees of social connectivity. High performance is achieved for low versus high valence post classification using writing style as features. The finding suggests the potential of using social media in depression screening, especially in online setting.
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Title of book
Web information systems engineering -- WISE 2014 : 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, proceedingsVolume
8786Series
Lecture Notes in Computer Science; v.8786Chapter number
30Pagination
398 - 407Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319117485Language
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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