Depressive Moods of Users Portrayed in Twitter

TLDR

Online social network data offer tremendous potential for clinical depression studies. The study aims to build a research framework that uses real‑time Twitter moods to explore language describing depressive moods. We analyzed a random two‑month sample of general Twitter posts and then studied 69 participants to compare sentiment word usage between depressed and typical users. The study revealed abundant depression‑related content on Twitter, including detailed feelings and treatment histories, and showed that users with major depressive symptoms used more negative and anger‑related words, though positive‑emotion word use did not differ, supporting the usefulness of online networks for capturing depressive moods.

Abstract

Potential benets 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. We found remarkable activities related to depression 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 diered from a typical user. We found that the use of words related to negative emotions and anger signicantly increased among Twitter users with major depressive symptoms compared to those otherwise. However, no dierence 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.