SocRoutes

Abstract

Location-based services, and in particular personal navigation systems, have become increasingly popular with the widespread use of GPS technology in smart devices. Existing navigation systems are designed to suggest routes based on the shortest distance or the fastest time to a target. In this paper, we propose a new type of route navigation based on regional context---primarily sentiments. Our system, called SocRoutes, aims to find a safer, friendlier, and more enjoyable route based on sentiments inferred from real-time, geotagged messages from Twitter. SocRoutes tailors routes by avoiding places with extremely negative sentiments, thereby potentially finding a safer and more enjoyable route with marginal increase in total distance compared to the shortest path. The system supports three types of traveling modes: walking, bicycling, and driving. We validated the idea based on crime history data from the City of Chicago Portal in December 2012, and sentiments extracted from geotagged tweets during the same time. We discovered that there was a significant correlation between regional Twitter posting sentiments and crime rate, in particular for high-crime and highly negative sentiment areas. We also demonstrated that SocRoutes, by solely utilizing social media sentiments, can find routes that bypass crime hotspots.

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