Consumers' Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China.

Online game products have fueled the boom in China's digital economy. Meanwhile, its public health concerns have sparked discussion among consumers on social media. However, past research has seldom studied the public health topics caused by online games from the perspective of consumer opinions. This paper attempts to identify consumers' opinions on the health impact of online game products through non-structured text and large-size social media comments. Thus, we designed a natural language processing (NLP) framework based on machine learning, which consists of topic mining, multi-label classification, and sentimental analysis. The hierarchical clustering method-based topic mining procedure determines the compatibility of this study and previous research. Every three topics are identified in "Personal Health Effects" and "Social Health Effects", respectively. Then, the multi-label classification model's results show that 61.62% of 327,505 comments have opinions about the health effects of online games. Topics "Adolescent Education" and "Commercial Morality" occupy the top two places of consumer attention. More than 31% of comments support two or more topics, and the "Adolescent Education" and "Commercial Morality" combination also have the highest co-occurrence. Finally, consumers expressed different emotional preferences for different topics, with an average of 63% of comments expressing negative emotions related to the health attributes of online games. In general, Chinese consumers are most concerned with adolescent education issues and hold the strongest negative emotion towards the commercial morality problems of enterprises. The significance of research results is that it reminds online game-related enterprises to pay attention to the potential harm to public health while bringing about additional profits through online game products. Furthermore, negative consumer emotions may cause damage to brand image, business reputation, and the sustainable development of the enterprises themselves. It also provides the government supervision departments with an advanced analysis method reference for more effective administration to protect public health and promote the development of the digital economy.

Research on Chinese Consumers' Attitudes Analysis of Big-Data Driven Price Discrimination Based on Machine Learning.

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers' attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, this article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics "Regular Customers Priced High" (69%) and "Usage Intention" (67%) occupy the top two places of negative sentiment among consumers, and the topic "Precision Marketing" (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers' comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers' psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness.

Impact of income inequality on CO2 emissions in G20 countries.

Over the past three decades, the G20 countries have experienced rapid economic growth but also a widening income disparity and deteriorating environment. We examined whether and how income distribution affects CO2 emissions during economic growth under the extended EKC framework. Using simultaneous quantile regression analysis, we show that, for developing countries, a more equal income distribution favors reductions to the CO2 emissions per capita, whereas, in most developed countries, income inequality hardly affects CO2 emissions. Meanwhile, the EKC hypothesis is valid in G20. Based on the empirical results, we particularly emphasize the importance of reducing income inequality in developing countries and that the entire G20 takes the path of sustainable development.