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Nearly all previous work on geo-locating latent states and activities fromsocial media confounds general discussions about activities, self-reports ofusers participating in those activities at times in the past or future, andself-reports made at the immediate time and place the activity occurs.Activities, such as alcohol consumption, may occur at different places andtypes of places, and it is important not only to detect the local regions wherethese activities occur, but also to analyze the degree of participation in themby local residents. In this paper, we develop new machine learning basedmethods for fine-grained localization of activities and home locations fromTwitter data. We apply these methods to discover and compare alcoholconsumption patterns in a large urban area, New York City, and a more suburbanand rural area, Monroe County. We find positive correlations between the rateof alcohol consumption reported among a community's Twitter users and thedensity of alcohol outlets, demonstrating that the degree of correlation variessignificantly between urban and suburban areas. While our experiments arefocused on alcohol use, our methods for locating homes and distinguishingtemporally-specific self-reports are applicable to a broad range of behaviorsand latent states.

Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities