Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China

The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.

Teleconsultation management in healthcare during the COVID-19 pandemic: The impact of Perceived Justice on satisfaction and Word-Of-Mouth

Following the restrictions on movement imposed during the COVID-19 pandemic, the greater demands placed on physicians and many healthcare infrastructures, a large number of health teleconsultation platforms have emerged around the world. The aim of this paper is to obtain a better understanding of the acceptance of teleconsultation platforms by patients. To this end, a survey of 1273 patients was carried out using a teleconsultation platform during the pandemic period in France (from 9 July to 29 October 2020). The questionnaire mobilized existing scales to measure Satisfaction, Perceived Justice and, finally, the intention to disseminate by Word-Of-Mouth (WOM). Data were analysed using a Partial Least Squares approach. The study confirms the impact of Distributive, Informational and Procedural Justice on Satisfaction and the impact of Distributive and Informational Justice on WOM. The findings emphasize the influence of Satisfaction on WOM. The results detect a mediating effect of Satisfaction on WOM and moderating effects of gender, age and long-standing use. This research contributes to both theoretical and practical COVID-19 research and may be used by healthcare professionals to develop teleconsultation services, one of the means of supporting interaction and satisfying patients’ treatment requirements during the pandemic.

Individual Decision Model for Using technology in Digital Era

Digital transformation has brought about great social changes, and individuals are constantly facing the challenge of using emerging technologies. This article, for the first time, combines the Diffusion of Innovation Theory and Contract Theory to build a decision model to solve the above challenge. The decision model is constructed according to the key factors that influence the individual decision process, including technological relative advantages, intrinsic motivation, risk-taking, use-cost, technological complexity and compatibility. Through the analysis of the cost utility of each party in Health CrowdSensing technology, the question of whether individuals use the technology is transformed into the question of cost utility. In the experiments, the validity of the decision model is verified by numerical analysis. The decision model proposed in this article provides theoretical basis and experimental verification for further research on how an individual decides whether to use technology or not.