The Expression of Happiness in Social Media of Individuals Reporting Depression

Depression has long been studied in the NLP field, with most works focusing on individuals’ negative emotions. People with depression experience happiness, but this has not been extensively studied. Previous works have shown that sentiment or emotion classification approaches are unsuitable for extracting happy moments because they may not be expressed only in positive words. In this work, we conduct a large-scale study of happy moments from social media texts of individuals mentioning a depression diagnosis. We develop an extensive deep learning-based framework to extract happy moments from text, and annotate them with semantic topics, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of users in the depression and control groups, varying by gender. We found that male and female users in the depression group expressed more sociality in their happy moments than control users. Furthermore, male users’ agency was not impaired in depression, while female users in the depression group expressed fewer happy moments with agency than the control group. Our research can inform psychology interventions, which can foster feelings of longer-lasting happiness and represent a promising path of collaboration between computational linguistics and psychology.