Social Sensors Based Online Attention Computing of Public Safety Events

Nowadays, the probability of public safety events around the world increase quickly. Recently, with the development of mobile network and intelligent mobile phones, social media users play an important role of the evolution and management of a public safety event. One of the important functions of Weibo is to monitor real time public safety events, such as fire, explosion, traffic jam, etc. Weibo users can be seen as social sensors and Weibo can be seen as the sensor platform. In this paper, a crowdsensing based online attention computing method of public safety events is proposed. The proposed method contains three steps. First, a mobile crowdsensing based social media crawler is given. Second, spatial and temporal information is used to analyze the online attention of the public safety event. At last, the proposed model based online attention governance system is given. The system collected the online attention data from Weibo. Besides, given the Weibo posts related to a detected public safety event, the proposed method targets at mining the multi-modal information, as well as storytelling the online attention of the public safety event precisely and concisely. Extensive experiment studies on real-world microblog datasets to demonstrate the superiority of the proposed framework. Case studies on real data sets show the proposed model has good performance and high effectiveness in the analysis of public safety events.