Abstract
Abstract It is reported that there are hundreds of thou- sands of deaths caused by seasonal flu all around the world every year. More other diseases such as chickenpox, malaria, etc. are also serious threats to people’s physical and mental health. There are 250,000–500,000 deaths every year around the world. Therefore proper techniques for disease surveillance are highly demanded. Recently, social media analysis is regarded as an efficient way to achieve this goal, which is feasible since growing number of people have been posting their health information on social media such as blogs, personal websites, etc. Previous work on social media analysis mainly focused on English materials but hardly considered Chinese materials, which hinders the application of such technique to Chinese peo- ple. In this paper, we proposed a new method of Chinese social media analysis for disease surveillance. More specifically, we compared different kinds of methods in the process of classification and then proposed a new way to process Chinese text data. The Chinese Sina micro-blog data collected from September to December 2013 are used to validate the effectiveness of the proposed method. The results show that a high classification precision of 87.49 % in average has been obtained. Comparing with the data from the authority, Chinese National Influenza Center, we can predict the outbreak time of flu 5 days earlier.