Using Twitter for Next-Place Prediction, with an Application to Crime Prediction

Abstract

This research focuses on two problems. First, we investigate the prediction of social media users' spatial trajectories. Recent work on this task has focused on the use of cellular network traces and location-based social network services such as Foursquare, all of which emit structured geospatial information (e.g., Cellular tower identifiers, GPS coordinates, and venue identifiers). Less attention has been paid to the rich textual content that users often publish in tandem with the structured information. We investigate methods of integrating textual content into existing next-place prediction models, and we demonstrate a significant improvement in next-place prediction compared to several baselines derived from published research. Second, we examine the correlation between these next-place predictions and the occurrence of crimes in a major United States city, with the goal of aiding future research into automatic crime prediction.

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