This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

This paper aims to examine the net effect of privacy fatigue of social media users on privacy protection disengagement behaviour, which is helpful to address the users’ privacy issue in the new stage of social media digitalization. Applying the Propensity Score Matching(PSM) methodology, the authors conduct the data analysis of 1,734 samples of social media users and eliminates the selectivity error caused by individual characteristic variables so as to improve the prediction accuracy of variable causality. Their research not only validates the causal relationship between privacy fatigue and privacy protection disengagement, proving that privacy fatigue can directly lead to privacy protection disengagement behaviour but also reveals that the individual characteristic variables have heterogeneous effects on the influence of privacy fatigue on protection disengagement behaviour.

The outbreak of terrorist events often causes tremendous damage to the country and society and arouses high attention from the public and an overwhelming response on the microblogging platform. Predicting the influence of microblogging in the context of terrorist events and revealing its evolutionary mode can help counterterrorism departments foresee potential risks, take effective countermeasures in time, and provide a reference for reducing public panic caused by terrorist events. In this study, Word2Vec is combined with the K-means clustering technique to discover the topics of microblogging, and an emotion analysis of microblogging is performed. The user features, time features, and content features of microblogging in the context of terrorist events are extracted. The prediction model of microblogging influence based on the logistic regression model was constructed and evaluated. The experimental results showed that the prediction accuracy of the model was 85.8%, which had superior performance over other six classification models. In addition, the high-influence characteristics of microblogging in the context of terrorist events were analyzed and summarized. Finally, a quantitative method of the influence of a microblogging topic based on the h-index was proposed. The evolution pattern of the influence of a microblogging topic was analyzed. The results can help predict microblog entries of high influence, understand the intensity and variation of public concern over terrorist events, and assist counterterrorism departments in taking scientific decisions.

This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.