StoryLine: Unsupervised Urban Geo-event Demultiplexing on Social Media without Location Information

Some of the most widely deployed IoT devices in urban areas are smart phones in the possession of urban individuals. Their proliferation has led to the emergence of crowdsensing/crowdsourcing services, where humans collect data about their environment (using phones), and servers aggregate the data for various application purposes of interest. With the emergence of social media, a common alternative form of human data entry has become media posts (e.g., on Twitter). This leads to the prospect of building crowdsensing services on top of social media content, exploiting humans as “sensors”. In this paper, we develop one such service, called StoryLine. The service detects and tracks physical urban events of interest to the user, such as car accidents, infrastructure damage (in the aftermath of a natural disaster), or instances of civil unrest. It offers an interface to client-side software that allows browsing such events in real time, as well as an interface for software applications to a structured representation of the events and their related statistics. The service embodies novel algorithms for real-time detection, demultiplexing, and tracking of physical events using social media data. In our evaluation with Twitter feeds, we show that our service outperforms two state-of-the-art baselines in event detection and demultiplexing. We also conduct two casestudies to show the effectiveness of the real-time event detection capability and event tracking performance of our system.