  Working adults spend nearly one third of their daily time at their jobs. In
this paper, we study job-related social media discourse from a community of
users. We use both crowdsourcing and local expertise to train a classifier to
detect job-related messages on Twitter. Additionally, we analyze the linguistic
differences in a job-related corpus of tweets between individual users vs.
commercial accounts. The volumes of job-related tweets from individual users
indicate that people use Twitter with distinct monthly, daily, and hourly
patterns. We further show that the moods associated with jobs, positive and
negative, have unique diurnal rhythms.
