Microblog Topic Contagiousness Measurement and Emerging Outbreak Monitoring.

A recent study on collective attention in Twitter shows that an epidemic spreading of hashtags is predominantly driven by external factors. We extend a time-series form of susceptible-infectious-recovered (SIR) model to monitor microblog emerging outbreaks by considering both endogenous and exogenous drivers. In addition, we adopt partially labeled Dirichlet allocation (PLDA) model to generate both background latent topics and hashtag topics. It overcomes the problem of small available samples in hashtag analysis by including related but unlabeled tweets through inference. We standardize hashtag topic contagiousness measure as the estimated effective-reproduction-number R derived from epidemiology. It is obtained by Bayesian parameter estimation. Guided by R, one can profile and categorize emerging topics, and generate alerts on potential outbreaks. Experiment results confirm the effectiveness of this approach.