earticle

영어

Real-time monitoring the spread of disease and taking a rapid response is necessary. The traditional public health report is accurate, but requires a lot of manpower and resources. The main drawback is the lag of time. Notifiable infectious diseases report generally lags behind medical diagnosis about 4-5 weeks. In this paper, social network data are used to detect disease and track its rapidly changing trends. We take flu data in Sina weibo as an example and analyze flu related weibos from temporal and spatial dimensions. Compared with the previous work, most studies filter out the non-infection weibo noises directly, however, the noises have close association with flu activities. We are not simply discard these data but use them to capture the public nuanced attitude changes. Flu related weibos are divided into four categories which represent four states of public concern. The four states, gradually upgrading from concern about news to anxiety of illness, help to capture the public nuanced attitude changes toward flu trends. Flu weibos concern distributed map and influenza activities curve are drawn to show the analyze result. Multiple classification systems’ accuracy are investigated. The proposed method twice iterative classification makes the system accuracy up to 89.50%.