  Nearly all previous work on geo-locating latent states and activities from
social media confounds general discussions about activities, self-reports of
users participating in those activities at times in the past or future, and
self-reports made at the immediate time and place the activity occurs.
Activities, such as alcohol consumption, may occur at different places and
types of places, and it is important not only to detect the local regions where
these activities occur, but also to analyze the degree of participation in them
by local residents. In this paper, we develop new machine learning based
methods for fine-grained localization of activities and home locations from
Twitter data. We apply these methods to discover and compare alcohol
consumption patterns in a large urban area, New York City, and a more suburban
and rural area, Monroe County. We find positive correlations between the rate
of alcohol consumption reported among a community's Twitter users and the
density of alcohol outlets, demonstrating that the degree of correlation varies
significantly between urban and suburban areas. While our experiments are
focused on alcohol use, our methods for locating homes and distinguishing
temporally-specific self-reports are applicable to a broad range of behaviors
and latent states.
