This study collected 2 million posts and reposts regarding the early stage of COVID-19 in China on Weibo from 26 December 2019 to 29 February 2020. Emotion analysis and social network analysis were used to examine the flow of emotional messages (emotion flow) by comparing them with the flow of general messages (information flow). Results indicated that both emotional messages and general messages present a multilayer diffusion pattern and follow network step flow models. In our dataset, emotion network has a higher transmission efficiency than information network; officially verified accounts were more likely to become super-spreaders of emotional messages; good emotions were predominant but isolated from other six emotions (joy, sadness, fear, disgust, surprise, anger) in online discussions; finally, government played a vital role in spreading good emotions.

Much research has shown that online news engenders greater political participation, but less attention has been paid to how the relationship can be suppressed by government online surveillance and censorship, especially as Internet freedoms continue to decline in many parts of the world. Drawing from 2017–20 World Value Survey and Varieties of Democracy project data, we conducted multilevel analyses across forty-four countries from seven continents that have different political and media systems. Results showed that online news and online surveillance were positively related to political engagement while online censorship was negatively related. Cross-level interactions also showed some support for the informational theory of repression, whereby the relationships among online news, surveillance, and engagement were conditioned at different levels of online censorship. The results suggest that while country-level online surveillance and censorship is highly correlated, varying levels can engender or suppress political engagement in different ways, which have implications for future studies on the dynamics of government digital repression and citizen participation in politics from a global comparative perspective.

The number of female gamers has grown rapidly in recent years, and female-oriented games appeal to a large market. This study focused on the cultivation effects of playing female-oriented dating sims on romantic beliefs and gender attitudes toward sexual relationships. It also investigated the mediating effects of parasocial relationships between game exposure and attitude outcomes. A survey of 284 participants in China was used to test the hypotheses. The findings show that game exposure was positively related to avatar identification, parasocial relationships, and romantic beliefs, and both avatar identification and parasocial relationships had positive associations with romantic beliefs and gender attitudes. Furthermore, there were strong mediation effects of avatar identification and parasocial relationships between game exposure and romantic beliefs. Public Policy Relevance StatementThis study examined dating sims, a popular game genre among young female gamers. The findings indicate that long-term exposure to dating sims might play an important role in shaping young female's romantic beliefs and gender attitudes toward sexual relationships. Moreover, players can identify themselves as the avatar and establish parasocial relationships with virtual characters, increasing media involvement and intensifying the game effects.

To evaluate and establish a prediction model of the outcome of induced labor based on machine learning algorithm. This was a cross-sectional design. The subjects were divided into primipara and multipara, and the risk factors for the outcomes of induced labor were assessed by multifactor logistic regression analysis. The outcome model of labor induced with oxytocin (OT) was constructed based on the four machine learning algorithms, including AdaBoost, logistic regression, naive Bayes classifier, and support vector machine. Factors, such as accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified. A total of 907 participants were included in this study. Logistic regression algorithm obtained better results in both primipara and multipara groups compared to the other three models. The accuracy of the model for the prediction of “successful induction of labor” was 94.24% and 96.55%, and that of “failed induction of labor” was 65.00% and 66.67% in the primipara and the multipara groups, respectively. This study established a prediction model of OT-induced labor based on the Logistic regression algorithm, with rapid response, high accuracy, and strong extrapolation, which was critical for obstetric clinical nursing.

No abstract