Data-Driven Power Outage Detection by Social Sensors

This paper proposes a novel method to detect and locate power outages based on the information collected from social media. Twitter is used as a real-time social sensor in the proposed method. To solve the challenges of detecting a targeted event from the fragmented and noisy tweets, we devise a probabilistic framework to integrate the textual, temporal, and spatial information to identify the event. To improve the accuracy of outage detection, we propose a supervised topic model with a heterogeneous information network. The proposed technique is tested with real tweets and outage cases. The numerical results demonstrate the effectiveness of the proposed methodology. The comparison between the proposed method, and support vector machine and statistics Bayesian method shows the accuracy of the developed model.