Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Exploring students' cognitive abilities has long been an important topic in education. This study employs data-driven artificial intelligence (AI) models supported by explainability algorithms and PSM causal inference to investigate the factors influencing students' cognitive abilities, and it delved into the differences that arise when using various explainability AI algorithms to analyze educational data mining models. In this paper, five AI models were used to model educational data. Subsequently, four interpretable algorithms, including feature importance, Morris Sensitivity, SHAP, and LIME, were used to globally interpret the results, and PSM causal tests were performed on the factors that affect students' cognitive abilities. The results reveal that self-perception and parental expectations have a certain impact on students' cognitive abilities, as indicated by all algorithms. Our work also uncovers that different explainability algorithms exhibit varying preferences and inclinations when interpreting the model, as evidenced by discrepancies in the top ten features highlighted by each algorithm. Morris Sensitivity presents a more balanced perspective, while SHAP and feature importance reflect the diversity of interpretable algorithms, and LIME shows a unique perspective. This detailed observation highlights the practical contribution of interpretable AI algorithms in the field of educational data mining, paving the way for more refined applications and deeper insights in future research.

Government social media is widely used for providing updates to and engaging with the public in the COVID-19 pandemic. While Facebook is one of the popular social media used by governments, there is only a scant of research on this platform. This paper aims to understand how government social media should be used and how its engagement changes in prodromal, acute and chronic stages of the pandemic. We collected 1664 posts and 10,805 comments from the Facebook pages of the Macao government from 1 January to 31 October 2020. Using word frequency and content analysis, the results suggest that the engagement was relatively low at the beginning and then surged in the acute stage, with a decreasing trend in the chronic stage. Information about public health measures maintained their engagement in all stages, whereas the engagement of other information was dropping over time. Government social media can be used for increasing vigilance and awareness in the prodromal stage; disseminating information and increasing transparency in the acute stage; and focusing on mental health support and recovery policies in the chronic stage. Additionally, it can be a tool for controlling rumors, providing regular updates and fostering community cohesion in public health crises.

Natural language processing (NLP), which is known as an emerging technology creating considerable value in multiple areas, has recently shown its great potential in government operations and public administration applications. However, while the number of publications on NLP is increasing steadily, there is no comprehensive review for a holistic understanding of how NLP is being adopted by governments. In this regard, we present a systematic literature review on NLP applications in governments by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The review shows that the current literature comprises three levels of contribution: automation, extension, and transformation. The most-used NLP techniques reported in government-related research are sentiment analysis, machine learning, deep learning, classification, data extraction, data mining, topic modelling, opinion mining, chatbots, and question answering. Data classification, management, and decision-making are the most frequently reported reasons for using NLP. The salient research topics being discussed in the literature can be grouped into four categories: (1) governance and policy, (2) citizens and public opinion, (3) medical and healthcare, and (4) economy and environment. Future research directions should focus on (1) the potential of chatbots, (2) NLP applications in the post-pandemic era, and (3) empirical research for government work.

Doctor–patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients’ attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper.