Analysis of user-generated content from online social communities to characterise and predict depression degree

Iram Fatima*, Hamid Mukhtar, Hafiz Farooq Ahmad, Kashif Rajpoot

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


The identification of a mental disorder at its early stages is a challenging task because it requires clinical interventions that may not be feasible in many cases. Social media such as online communities and blog posts have shown some promising features to help detect and characterise mental disorder at an early stage. In this work, we make use of user-generated content to identify depression and further characterise its degree of severity. We used the user-generated post contents and its associated mood tag to understand and differentiate the linguistic style and sentiments of the user content. We applied machine learning and statistical analysis methods to discriminate the depressive posts and communities from non-depressive ones. The depression degree of a depressed post is identified using variations of valence values based on the mood tag. The proposed methodology achieved 90%, 95% and 92% accuracy for the classification of depressive posts, depressive communities and depression degree, respectively.

Original languageEnglish
Pages (from-to)683-695
Number of pages13
JournalJournal of Information Science
Issue number5
Publication statusPublished - 1 Oct 2018

Bibliographical note

Funding Information:
This research is supported by the King Faisal University-Deanship of Scientific Research (DSR) for project id 160090.

Publisher Copyright:
© The Author(s) 2017.


  • Depression classification
  • depression degree identification
  • mental health
  • moods and emotions
  • online communities

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences


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