Practical Study Regarding Social Sensing Technologies for Extracting Unordinary Phenomena Considering User Attributes with Focus on Different Behavior from Normal Time

Abstract

With the spread of CGM (Consumer Generated Media), a huge amount of digital data has been accumulated on the Internet. These data are utilized for improving social sensing technologies to measure not only social and economic trends but also various kinds of phenomena such as large-scale disasters. Using habitual behavior of users, authors proposed a new social sensing method for extracting phenomena in the actual world from the difference in the habitual behavior, and proved its usefulness. To practically evaluate the versatility of the application examples of the technology, it is necessary to clarify whether the data with different user attributes are also applicable. In this study, the habitual behavior of the users is analyzed attribute by attribute, and abnormal behavior is extracted based on different behavior from the normal time for each user attribute. Demonstration experiments were conducted to verify whether it is possible to find out social trends in the actual world or social phenomena for each user attribute on a detailed granularity.