The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.

COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.

Background COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. Objective This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. Methods We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. Results This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. Conclusions This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.

COVID-19 has become one of the most widely talked about topics on social media. This research characterizes risk communication patterns by analyzing the public discourse on the novel coronavirus from four Asian countries: South Korea, Iran, Vietnam, and India, which suffered the outbreak to different degrees. The temporal analysis shows that the official epidemic phases issued by governments do not match well with the online attention on COVID-19. This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics. Here, we propose an automatic method to detect topical phase transitions and compare similarities in major topics across these countries over time. We examine the time lag difference between social media attention and confirmed patient counts. For dynamics, we find an inverse relationship between the tweet count and topical diversity.

Currently, because of stricter environmental standards and highly competitive markets, machining operations, as the main part of the manufacturing cycle, need to be rigorously optimized. In order to simultaneously maximize the production quality and minimize the environmental issues related to the grinding process, this research study evaluates the performance of minimum quantity lubrication (MQL) grinding using water-based nanofluids in the presence of horizontal ultrasonic vibrations (UV). In spite of the positive impacts of MQL using nanofluids and UV which are extensively reported in the literature, there is only a handful of studies on concurrent utilization of these two techniques. To this end, for this paper, five kinds of water-based nanofluids including multiwall carbon nanotube (MWCNT), graphite, Al2O3, graphene oxide (GO) nanoparticles, and hybrid Al2O3/graphite were employed as MQL coolants, and the workpiece was oscillated along the feed direction with 21.9 kHz frequency and 10 µm amplitude. Machining forces, specific energy, and surface quality were measured for determining the process efficiency. As specified by experimental results, the variation in the material removal nature made by ultrasonic vibrations resulted in a drastic reduction of the grinding normal force and surface roughness. In addition, the type of nanoparticles dispersed in water had a strong effect on the grinding tangential force. Hybrid Al2O3/graphite nanofluid through two different kinds of lubrication mechanisms—third body and slider layers—generated better lubrication than the other coolants, thereby having the lowest grinding forces and specific energy (40.13 J/mm3). It was also found that chemically exfoliating the graphene layers via oxidation and then purification prior to dispersion in water promoted their effectiveness. In conclusion, UV assisted MQL grinding increases operation efficiency by facilitating the material removal and reducing the use of coolants, frictional losses, and energy consumption in the grinding zone. Improvements up to 52%, 47%, and 61%, respectively, can be achieved in grinding normal force, specific energy, and surface roughness compared with conventional dry grinding.