  In this work, we explore the relationship between depression and
manifestations of happiness in social media. While the majority of works
surrounding depression focus on symptoms, psychological research shows that
there is a strong link between seeking happiness and being diagnosed with
depression. We make use of Positive-Unlabeled learning paradigm to
automatically extract happy moments from social media posts of both controls
and users diagnosed with depression, and qualitatively analyze them with
linguistic tools such as LIWC and keyness information. We show that the life of
depressed individuals is not always bleak, with positive events related to
friends and family being more noteworthy to their lives compared to the more
mundane happy events reported by control users.
