Research on Migrant Works' Concern Recognition and Emotion Analysis Based on Web Text Data

Cyber violence caused by the disclosure of route information during the COVID-19 pandemic

Disclosure of patients' travel route information by government departments has been an effective and indispensable pandemic prevention and control measure during the COVID-19 pandemic. However, this measure may make patients susceptible to cyber violence (CV). We selected 13 real cases that occurred in China during the COVID-19 pandemic for analysis. We identified several characteristics that commonly appeared due to route information, such as rumors about and moral condemnation of patients, and determined that patients who are the first locally confirmed cases of a particular wave of the pandemic are more likely to be the victims of CV. We then analyzed and compared six real cases using data mining and network analysis approaches. We found that disclosing travel route information increases the risk of exposing patients to CV, especially those who violate infection prevention regulations. In terms of disseminating information, we found that mainstream media and influential we-media play an essential role. Based on the findings, we summarized the formation mechanism of route information disclosure-caused CV and proposed three practical suggestions-namely, promote the publicity of the media field with the help of mainstream media and influential we-media, optimize the route information collection and disclosure system, and ease public anxiety about the COVID-19 pandemic. To our knowledge, this study is one of the first to focus on CV on social media during the COVID-19 pandemic. We believe that our findings can help governments better carry out pandemic prevention and control measures on a global scale.

The Age of Mobility: Can Equalization of Public Health Services Alleviate the Poverty of Migrant Workers?

Migrants workers are important participants in and contributors to economic and social construction, but they still face the reality of being marginalized. Based on data from the China Migrants Dynamic Survey in 2018, this paper systematically investigated the impact of public health services on the multidimensional poverty of migrant workers. The research found that, first, the current mean of the multidimensional poverty deprivation value of migrant workers is 0.1806, which is one dimension of poverty that exists on average. In addition, migrant workers do not have high access to public health services. The proportions of migrant workers who have not established residents' health files and who have not received public health education are 74.22% and 29.92%, respectively. Second, public health services can significantly alleviate the multidimensional poverty of migrant workers. After mitigating the potential endogeneity problem by the IV-2SLS method and conducting robustness tests by the PSM method, the conclusion is still robust. Further research found that the impact of public health services on the multidimensional poverty alleviation of migrant workers is heterogeneous. The improvement of public health services has the greatest effect on the multidimensional poverty alleviation of the new generation of migrant female workers in the western region. The research in this paper helps to examine and clarify the policy significance of public health services for the multidimensional poverty alleviation of migrant workers and provides empirical evidence for the use of public health services to tackle the poverty problem.

Customer Perceived Risk Measurement with NLP Method in Electric Vehicles Consumption Market: Empirical Study from China

In recent years, as people’s awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers’ perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of “Technology Maturity” (A13) which reached 25,329, and which belongs to the “Performance Risk” (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the “Social Risk” (PR5) dimension, the abstract “Social Needs” (A51) received only 3224 comments and “Preference and Trust Rank” (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension’s emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the “Time Risk” (PR4), accounting for 54%. On a finer scale, the top three negative emotions are “Charging Time” (A42), “EV Charging Facilities” (A41), and “Maintenance of Value” (A33). Another interesting result is that “Social Needs” (A51)’s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers’ perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs’ consumption market in China.