Spatio-Temporal Web Sensors by Social Network Analysis

Many researches on mining the Web, especially Social Networking Media such as web logs and microblogging sites which seem to store vast amounts of information about human societies, for knowledge about various phenomena and events in the physical world have been done actively, and Web applications with Web-mined knowledge have begun to be developed for the public. However, there is no detailed investigation on how accurately Web-mined data reflect real-world data. It must be problematic to idolatrously utilize the Web-mined data in public Web applications without ensuring their accuracy sufficiently. Therefore, this paper defines spatio-temporal Web Sensors by analyzing Twitter, Facebook, web logs, news sites, or the whole Web for a target natural phenomenon, and tries to validate the potential and reliability of the Web Sensors´ spatio-temporal data by measuring the coefficient correlation with Japanese weather, earthquake, and influenza statistics per week by region as real-world data.