Differentiating sub-groups of online depression-related communities using textual cues

Thin Nguyen, Bridianne O’Dea, Mark Larsen, Dinh Phung, Svetha Venkatesh, Helen Christensen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2015
Subtitle of host publication16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II
EditorsJianyong Wang, Wojciech Cellary, Dingding Wang, Hua Wang, Shu-Ching Chen, Tao Li, Yanchun Zhang
Place of PublicationCham Switzerland
Number of pages9
ISBN (Electronic)9783319261874
ISBN (Print)9783319261867
Publication statusPublished - 2015
Externally publishedYes
EventInternational Conference on Web Information Systems Engineering 2015 - Miami, United States of America
Duration: 1 Nov 20153 Nov 2015
Conference number: 16th
https://link.springer.com/book/10.1007/978-3-319-26190-4 (Conference Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Web Information Systems Engineering 2015
Abbreviated titleWISE 2015
Country/TerritoryUnited States of America
Internet address


  • Feature extraction
  • Online depression
  • Textual cues
  • Web community

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