Knowing that the demand for advertising in a growing e-commerce market cannot be sufficiently addressed in a traditional advertising operation model, advertisers and e-commerce platforms apply artificial intelligence (AI) technologies in advertising to improve efficiency and meet the market demand. From observations in the Chinese advertising market over the past five years, the authors gain insight into how AI technologies are applied in the advertising process and propose that the advertising process powered by AI technologies is composed of four steps: consumer insight discovery, ad creation, media planning and buying, and ad impact evaluation. This new advertising process, which is supported by a data-based platform with algorithms at its core, is tool based, synchronized, and highly efficient. AI has reorganized and upgraded the traditional advertising process and improved advertising efficiency; however, the new process is still born out of the traditional process and is not yet a reengineered one.

Background: Considerable research has been devoted to examining the mental health conditions of patients with COVID-19 and medical staff attending to these patients during the COVID-19 pandemic. However, there are few insights concerning how the pandemic may take a toll on the mental health of the general population, and especially of nonpatients (ie, individuals who have not contracted COVID-19). Objective: This study aimed to investigate the association between social media use and mental health conditions in the general population based on a national representative sample during the peak of the COVID-19 outbreak in China. Methods: We formed a national representative sample (N=2185) comprising participants from 30 provinces across China, who were the first to experience the COVID-19 outbreak in the world. We administered a web-based survey to these participants to analyze social media use, health information support received via social media, and possible psychiatric disorders, including secondary traumatic stress (STS) and vicarious trauma (VT). Results: Social media use did not cause mental health issues, but it mediated the levels of traumatic emotions among nonpatients. Participants received health information support via social media, but excessive social media use led to elevated levels of stress (β=.175; P<.001), anxiety (β=.224; P<.001), depression (β=.201; P<.001), STS (β=.307; P<.001), and VT (β=.688; P<.001). Geographic location (or geolocation) and lockdown conditions also contributed to more instances of traumatic disorders. Participants living in big cities were more stressed than those living in rural areas (P=.02). Furthermore, participants from small cities or towns were more anxious (P=.01), stressed (P<.001), and depressed (P=.008) than those from rural areas. Obtaining more informational support (β=.165; P<.001) and emotional support (β=.144; P<.001) via social media increased their VT levels. Peer support received via social media increased both VT (β=.332; P<.001) and STS (β=.130; P<.001) levels. Moreover, geolocation moderated the relationships between emotional support on social media and VT (F2=3.549; P=.029) and the association between peer support and STS (F2=5.059; P=.006). Geolocation also interacted with health information support in predicting STS (F2=5.093; P=.006). Conclusions: COVID-19 has taken a severe toll on the mental health of the general population, including individuals who have no history of psychiatric disorders or coronavirus infection. This study contributes to the literature by establishing the association between social media use and psychiatric disorders among the general public during the COVID-19 outbreak. The study findings suggest that the causes of such psychiatric disorders are complex and multifactorial, and social media use is a potential factor. The findings also highlight the experiences of people in China and can help global citizens and health policymakers to mitigate the effects of psychiatric disorders during this and other public health crises, which should be regarded as a key component of a global pandemic response.

Many studies have examined the influence of digital technologies, such as robots and artificial intelligence, on enterprise labor, but few have investigated the underlying mechanisms and impact paths of digital empowerment on labor employment. Therefore, this study uses data on manufacturing enterprises listed on China's Shanghai and Shenzhen A-share markets from 2011 to 2020, and applies a panel fixed effect model to test the relationship between digital empowerment and labor employment, and the mechanisms underlying this relationship. We find that digital empowerment increases labor employment. However, the effects are heterogeneous: firms with better corporate governance, more competitive industry, and less favorable regional business environments are more motivated to optimize the structure of their labor resources. Through robustness test and mediation effect model test, we find that digital empowerment can improve enterprise human capital by increasing economies scale and managerial efficiency, especially the employment of R&D and innovation personnel and management personnel; it can also affect the amount of human capital by improving total factor productivity.

Information is passed by word-of-mouth figures prominently when consumers evaluate products through reviews. However, severe logistical problems are caused by the internets Water Army (i.e., literally people who are hired by individuals or organizations to compose false reviews), that flood the internet e-commerce websites. An array of internet e-commerce sites is flooded with inauthentic information, and false reviews are used to maliciously induce consumers to purchase specific products, that often contain some defects. Notwithstanding the fact that the internet Water Army first manifested in China, it can also exist in other countries. The rationale lies in the high profitability possible, in the minds of numerous organized underground paid poster groups, and in writing fake reviews to misinform consumers. It has become an increasingly daunting task to precisely spot the Water Army members, who often alter their writing style and posted content. In this paper, the authors devise a comprehensive set of features to characterize all users and compare the paid posters against the normal users on different dimensions; furthermore, an ensemble detection model equipped with seven disparate algorithms is put into place. Our model reached a score of 0.730 in the AUC measure, 0.691 in the F1 measure on the JD dataset, 0.926 in the AUC measure, and 0.871 in the F1 measure on the Amazon dataset, which outshines the measures in the existing research. The significance and contribution of this work are in advancing constructive solutions and recommendations for this major concern of the entire e-commerce industry.

BACKGROUND Considerable research has been devoted to examining the mental health conditions of patients with COVID-19 and medical staff attending to these patients during the COVID-19 pandemic. However, there are few insights concerning how the pandemic may take a toll on the mental health of the general population, and especially of nonpatients (ie, individuals who have not contracted COVID-19). OBJECTIVE This study aimed to investigate the association between social media use and mental health conditions in the general population based on a national representative sample during the peak of the COVID-19 outbreak in China. METHODS We formed a national representative sample (N=2185) comprising participants from 30 provinces across China, who were the first to experience the COVID-19 outbreak in the world. We administered a web-based survey to these participants to analyze social media use, health information support received via social media, and possible psychiatric disorders, including secondary traumatic stress (STS) and vicarious trauma (VT). RESULTS Social media use did not cause mental health issues, but it mediated the levels of traumatic emotions among nonpatients. Participants received health information support via social media, but excessive social media use led to elevated levels of stress (β=.175; P<.001), anxiety (β=.224; P<.001), depression (β=.201; P<.001), STS (β=.307; P<.001), and VT (β=.688; P<.001). Geographic location (or geolocation) and lockdown conditions also contributed to more instances of traumatic disorders. Participants living in big cities were more stressed than those living in rural areas (P=.02). Furthermore, participants from small cities or towns were more anxious (P=.01), stressed (P<.001), and depressed (P=.008) than those from rural areas. Obtaining more informational support (β=.165; P<.001) and emotional support (β=.144; P<.001) via social media increased their VT levels. Peer support received via social media increased both VT (β=.332; P<.001) and STS (β=.130; P<.001) levels. Moreover, geolocation moderated the relationships between emotional support on social media and VT (F2=3.549; P=.029) and the association between peer support and STS (F2=5.059; P=.006). Geolocation also interacted with health information support in predicting STS (F2=5.093; P=.006). CONCLUSIONS COVID-19 has taken a severe toll on the mental health of the general population, including individuals who have no history of psychiatric disorders or coronavirus infection. This study contributes to the literature by establishing the association between social media use and psychiatric disorders among the general public during the COVID-19 outbreak. The study findings suggest that the causes of such psychiatric disorders are complex and multifactorial, and social media use is a potential factor. The findings also highlight the experiences of people in China and can help global citizens and health policymakers to mitigate the effects of psychiatric disorders during this and other public health crises, which should be regarded as a key component of a global pandemic response.