Linearly-Combined Web Sensors for Spatio-temporal Data Extraction from the Web

Many researches on mining the Web, especially CGM (Consumer Generated Media) such as Web logs, for knowledge about various phenomena and events in the physical world have been done actively, and Web services with the 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 services without ensuring their accuracy sufficiently. Therefore, this paper defines the basic Web log Sensor with a neutral, positive, or negative description for a target phenomenon, and their linearly-combined Web log Sensors, and tries to validate the potential and reliability of these Web log Sensors´ spatio-temporal data by measuring the correlation with weather (precipitation) and earthquake (maximum seismic intensity and number of felt quakes) statistics per day by region of Japan Meteorological Agency as real-world data.