Background:This study investigates negative emotional patterns of people with depression on Sina Weibo™ and gives an in-depth analysis of how their negative emotions change over time.Materials and Methods:A text classifier using deep learning methods was built to identify people on Sina Weibo with depression and associated negative emotions. The longitudinal changes in negative emotions were assessed using time series and cluster analysis.Results:Results indicate that people with depression (n = 616) were more active and expressed more negative emotions on social media compared with control users (n = 3,176). Furthermore, negative emotions of people with depression were mostly about their depression issues, such as treatment, hopelessness, suicidal ideation, or self-injury. Both groups of users usually expressed negative emotions in the late evening and early morning hours. Finally, longitudinal changes in negative emotions illustrate that users with depression tended to have relatively high negative emotions in the month when they started using social media to reveal their depression issues and that they exhibited three main evolutionary patterns of negative emotions on social media.Conclusions:Findings from the study could be used to track and monitor negative emotional states of people with depression on social media, in addition to providing an in-depth understanding of how negative emotions change over time, so that better intervention strategies can be adopted to assist them.
Keywords: classifier; depression; longitudinal changes; negative emotions; social media; telemedicine.