Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis

Dao,B, Nguyen,T, Phung,D and Venkatesh,S 2014, Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis. In Benatallah,B, Bestavros,A, Manolopoulos,Y, Vakali,A and Zhang,Y (ed), , Springer, Berlin, Germany, pp.398-407, doi: 10.1007/978-3-319-11749-2_30.

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Title Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis
Author(s) Dao,B
Nguyen,TORCID iD for Nguyen,T orcid.org/0000-0003-3467-8963
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Editor(s) Benatallah,B
Publication date 2014
Series Lecture Notes in Computer Science; v.8786
Chapter number 30
Total chapters 40
Start page 398
End page 407
Total pages 10
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Depression communities
Mental health
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Computer Science, Theory & Methods
Computer Science
Summary Depression 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.
ISBN 9783319117485
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-11749-2_30
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
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072242

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