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

TL;DR

This paper introduces machine learning methods to infer detailed user activities and home locations from social media data, specifically detecting alcohol consumption patterns and their relation to local outlet density in different community types.

Contribution

The paper presents novel techniques for fine-grained activity localization and distinguishes temporally-specific self-reports from general discussions, applicable to various behaviors.

Findings

Positive correlation between alcohol reports and outlet density in NYC and Monroe County.

Methods successfully differentiate between activity reports and general discussions.

Application demonstrates potential for broad behavioral analysis from social media data.

Abstract

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.