China’s two-child policy, aimed at boosting the country’s total fertility rate, has failed to achieve the desired outcomes. Previous studies on low fertility rates mainly used data obtained from demographic censuses, questionnaires, or interviews. These data-gathering methods are costly, entailing time delays and yielding limited information. User-generated content (UGC) provides an alternative data source. We propose a machine–human hybrid approach using UGC obtained from social media to assess users’ intentions to have a second child. Our results showed that couples associate a second child with high economic costs mainly through negative impacts on the mothers’ careers, with no concomitant economic benefits. A key motivation for having two children relates to the mental benefit of the joy in having children. However, raising a second child also entails considerable mental costs such as exhaustion and pressure. Couples largely seek help within their extended families, that is, their parents are major sources of child-rearing support. Therefore, the government should devise ways of reducing the negative impacts of having a second child on a woman’s career and provide child-rearing support to help increase the fertility rate. Our proposed approach can also be used to elicit the reasons for low fertility rates in other countries.

Danmaku is an important means of interaction in online education, providing a learning atmosphere of collaboration with peers. Nowadays, there have been more studies on Danmaku interaction. However, there are still some shortcomings in the existing literature for classifying and quantifying the content of Danmaku interactions. Semi-structured interviews were conducted with 35 informants to identify learner-content interaction that met learners’ functional needs and learner-learner interactions that met social, hedonic, and emotional needs. Using content analysis of two video lectures, we found that learner-learner interaction accounted for one-third in both. More learner-content interaction occurred in the lecture with declarative knowledge (in which instructors encouraged learners to practice) than in the lecture with procedural knowledge (in which the learners followed fixed steps). Learner-instructor interaction was also identified—especially when instructors had specific personal characteristics. Learner-interface interaction was also evident—especially in lectures that had quality issues. Since the learner-instructor communication was only one-way (no response from instructors), it was not perceived by learners, indicating that learners’ desire was not satisfied by Danmaku. The results of the study can provide teachers and content creators with advice related to course production and help teaching faculty recognize the importance of one-to-many interactive language and Danmaku interactions for learners.

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

Understanding stressors is an effective measure to decrease employee stress and improve employee mental health. The extant literature mainly focuses on a singular stressor among various aspects of their work or life. In addition, the extant literature generally uses questionnaires or interviews to obtain data. Data obtained in such ways are often subjective and lack authenticity. We propose a novel machine–human hybrid approach to conduct qualitative content analysis of user-generated online content to explore the stressors of young employees in contemporary society. The user-generated online contents were collected from a famous Q&A platform in China and we adopted natural language processing and deep learning technology to discover knowledge. Our results identified three kinds of new stressors, that is, affection from leaders, affection from the social circle, and the gap between dream and reality. These new identified stressors were due to the lack of social security and regulation, frequent occurrences of social media fearmongering, and subjective cognitive bias, respectively. In light of our findings, we offer valuable practical insights and policy recommendations to relieve stress and improve mental health of young employees. The primary contributions of our work are two-fold, as follows. First, we propose a novel approach to explore the stressors of young employees in contemporary society, which is applicable not only in China, but also in other countries and regions. Second, we expand the scope of job demands-resources (JD-R) theory, which is an important framework for the classification of employee stressors.

As one of the first doctors issued a protective warning to the public, Dr. Li Wenliang was known as “whistleblower” of COVID-19 pandemic. After his death of COVID-19, students entered to his Sina Weibo to display their condolences and sorrow. We conduct text analysis and sentiment classification to investigate the motivation behind online mourning for Dr. Li among students on Sina Weibo. Our results indicate that, a) there always more than one motivation behind online mourning exists in each time period. b) continuing connection and semi-interaction with the deceased is the main motivation when students mourn online. c) there exists positive correlation between the influence of the deceased and the motivation--sharing information with the community of fans and creating social support in a time of loss and social support. d) the motivation--honoring the dead and expressing sadness and resentment can gradually lose over time.

Introduction: During the COVID-19 pandemic, various types of disinformation have emerged from the media. This study focuses on the online disinformation about tobacco and the COVID-19 on the Sina Weibo, the Chinese largest new media microblog platform. Material and Methods: The related posts...