Purpose This paper aims to selects 59 journals that focus on data science research in 14 disciplines from the Ulrichsweb online repository. This paper analyzes the aim and scope statement using both quantitative and qualitative methods to identify the research types and the scope of research promoted by these journals. Design/methodology/approach Multiple disciplines are involved in data science research and publishing, but there lacks an overview of what those disciplines are and how they relate to data science. In this study, this paper aims to understand the disciplinary characteristics of data science research. Two research questions are answered: What is the population of journals that focus on data science? What disciplinary landscape of data science is revealed in the aim and scope statements of these journals? Findings Theoretical research is mainly included in journals that belong to statistics, engineering and sciences. Almost all data science journals include applied research papers. Keywords analysis shows that data science research in computers, statistics, engineering and sciences appear to share characteristics. While in other disciplines such as biology, business and education, the keywords are indicative of the types of data to be used and the special problems in these disciplines. Originality/value This is the first study to use journals as the unit of analysis to identify the disciplines involved in data science research. The results provide an overview of how researchers and educators from different disciplinary backgrounds understand data science research.

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BACKGROUND During the pandemic of COVID-19, United States public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. OBJECTIVE The purpose of the study was to understand the variations of public sentiment towards COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, and how the variations in public sentiment are related to geographical and socioeconomic factors. METHODS The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19-related Tweets, from January 21, 2020, to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment towards both general COVID-19 topics and preventive practices in the United States. RESULTS In the temporal analysis, we found a four-stage pattern from high negative sentiment in the initial stage, to decreasing and low negative sentiment in the second and third stage, to the rebound and increase of negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. CONCLUSIONS The differences between public sentiment towards COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and the rebound stage in future pandemics. The urban/rural differences should be considered in terms of the communication strategies and decision-makings during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state level, which could guide local and state governments, and regional communities in making decisions and developing policies in crises. CLINICALTRIAL

The mechanism of information diffusion on social media platforms such as retweeting in Twitter has been largely studied. Existing studies mainly look into the social or discursive features of information that are popular in virtual space, few have related the diffusion of information to the real‐world context and studied the characteristics of information that can spread across nations. In the context of globalization, understanding information spreading across nations not only facilitates marketing and propagation but also helps to understand transnational activities such as transnational social movements. We conduct a preliminary study to analyze the sentiment and cognition components of tweets that disseminate transnationally in the MeToo movement. Tweets that spread across nations are generally popular, that is retweeted more. However, popular tweets do not always spread across nations. We find popular tweets that disseminate transnationally contain more elements of politics, religion, or social bond indicators. Our study provides insights for propagation in globalization and may help social movement organizations that aim for transnational activities.

Background During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. Objective The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors. Methods The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19–related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States. Results In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. Conclusions The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.