After the boom no one tweets: microblog based influenza detection incorporating indirect information
The recent rise in popularity and scale of social media services (SNSs) has created the growing necessity for SNS based information extraction systems. A popular application of SNS data is health surveillance for predicting the outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the same logic: they incorporate SNS users as social sensors. Such social sensor approaches also share a common problem: SNS based surveillance can be reliable if sufficiently numerous users are active, but small inactive populations produce inconsistent results. To overcome this problem, this paper presents a novel approach using indirect information covering both urban areas and rural areas within the posts. This system uses not only direct information, but also indirect information that mentions other places. Indirect information is less reliable (too noisy or too old) than direct information. Therefore, we do not use the described data directly. Instead, we consider that indirect information inhibits direct information. For example, when indirect information appears often, we consider that everyone already has a known disease, leading to a small amount of direct information. In the experiment using three years' long collection of tweets (7 million influenza related tweets in Japanese) described herein, this proposed approach improved the detection performance not only in rural cities, but also in urban cities, thereby demonstrating the feasibility of this approach.
EDB