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In the period of public health crisis, effective and efficient transmission of crisis information to the public through social media is an important support for achieving social stability and orderly online public engagement. From the perspective of public value management, this study systematically investigated how local government agencies in China used social media to promote public engagement and raise public sentiment during the COVID-19 crisis. Using data captured from the “Wuhan Release” Sina Weibo account, the authors studied the factors that influence public engagement, including information sources, language styles, and media types. Further, it explores the influence of the interactive effects of public value with information sources, language styles, and media types on public engagement and public sentiment. The results show that the consistency of government response content and public value promotes public engagement and raises public sentiment. This research provides enlightenment and ideas for cognition, understanding and governance of public opinion in practice.

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<p style='text-indent:20px;'>Support vector machines with Universum are attractive for dealing with classification problems by incorporating prior information. In this paper, a quadratic function based kernel-free support vector machine with Universum is proposed for binary classification. To deal with noise and outliers, two fuzzy membership functions considering both information entropy and distance information are constructed for labeled and Universum data, respectively. The fuzzy membership function for Universum is also adopted for further selecting Universum data to improve the robustness. The proposed model corresponds to an efficiently solved convex quadratic programming. In the meanwhile, by avoiding the issue of choosing kernel functions, the proposed model saves more computational time when compared with other Universum-based support vector machines. Finally, some numerical tests are implemented on several data sets to validate the classification effectiveness of the proposed method. The numerical results illustrate the competitive performance when compared with some state-of-the-art support vector machines. Applications on two credit rating data sets are also conducted to distinguish the classification performance of the proposed method.</p>

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