Depressive moods of users portrayed in Twitter

Minsu Park, Chiyoung Cha, Meeyoung Cha

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Potential benefits of using online social network data for clinical studies on depression are tremendous. In this paper, we present a preliminary result on building a research framework that utilizes real-time moods of users captured in the Twitter social network and explore the use of language in describing depressive moods. First, we analyzed a random sample of tweets posted by the general Twitter population during a two-month period to explore how depression is talked about in Twitter. A large number of tweets contained detailed information about depressed feelings, status, as well as treatment history. Going forward, we conducted a study on 69 participants to determine whether the use of sentiment words of depressed users differed from a typical user. We found that the use of words related to negative emotions and anger significantly increased among Twitter users with major depressive symptoms compared to those otherwise. However, no difference was found in the use of words related to positive emotions between the two groups. Our work provides several evidences that online social networks provide meaningful data for capturing depressive moods of users.
Original languageEnglish (US)
Title of host publicationProceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2012
Number of pages8
StatePublished - 2012


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