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Because posting on Sina Weibo has become the most popular and quickest way for spreading emergent news. However, meanwhile, the relevant management departments are obliged to deal with emergencies’ information on social media within a short time. Therefore, predicting the propagation effect of emergency microblog entries can help management departments find out the probable coming problems in time, and also improve the predictability of decision-making. In this paper, a method of predicting the propagation effect toward entries of main emergencies released by official media is proposed which has rarely been studied. We measure the propagation effect of emergency microblog entries from their repost, comment and favorite counts. In order to reach the target, user profiles, text features, interactive attributes were first extracted and verified separately. With these filtered multi-features, an improved model based on random forest is then constructed, trained and tested for predicting the publics’ interactive behaviors on Sina Weibo dataset. The results in our experiment demonstrate the effectiveness of our algorithm compared with most existing models.
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