Analysis of the Role of Individuals and Governments in Post-COVID-19 Health Issues: The Case of Korea

This study was conducted in order to ascertain what role government and individuals should play in the event of a pandemic such as Coronavirus occurring in Korea in the future, using information deriving from news articles available at the Bigkinds news portal site in Korea. The analysis period ran from 11 March 2020, when the pandemic was declared by the World Health Organization, to 31 January 2023, almost three years later. Text mining analysis was conducted on all the articles, as a result of which six important roles that individuals should play, and ten roles that government should play, in a pandemic situation were suggested. © 2022, European Alliance for Innovation. All rights reserved.

Understanding telemedicine service users' perceptions: A text mining analysis on social media discussion

Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study aims to understand telemedicine users' perceptions of their care services and identify the aspects of telemedicine that can be improved to enhance users' experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study. © 2023 IEEE Computer Society. All rights reserved.

Global Perception of Telemedicine before and after COVID-19 : A Text Mining Analysis

Determining the perception and sentiment of public's opinion of telemedicine and telecare has benefits to healthcare organizations, physicians and patients. Determining a relationship between opinion and demographic elements will aid in developing ways to close the gap between perception and readiness to implement healthcare technology for patients. The concept of telemedicine becomes more critical due to the onset of pandemics such as COVID-19. In addition, with telemedicine being a viable option to reduce cost and inconvenience for the patient, while delivering care that is effective and efficient, having patient buy in will be a key element. This study aims to identify the perception about telemedicine and telecare based on the posts by Twitter users eighteen months before and after COVID-19 pandemic. We leveraged VADER sentiment analysis model to identify the sentiment of the public using the tweets they posted. Out of approximately 1,073,817 tweets included, 491,695 unique tweets from 10,495 unique users met the inclusion criteria Among all countries, United States dominated the tweet volume. Among all the states in US, it is interesting to note that district of Columbia dominated the tweet volume. Among tweets from top five English speaking countries, interestingly after March 2020, the average sentiment of all countries seems to converge to the same value. Results indicate that before COVID-19 outbreak, people had neutral perception or sentiment towards telemedicine, while after the onset of increased cases and high alert situations, people tend to support Telemedicine and the overall perception started to grow towards the positive side. © 2022 IEEE.

Effects of members' response styles in an online depression community based on text mining and empirical analysis

The online depression community (ODC) has become a popular resource for people with depression to manage their mental health during the COVID-19 pandemic. This study proposed a novel perspective based on response style theory to investigate whether depression individuals' distractive and ruminative behaviors in ODC were related to social support received and co-rumination. Furthermore, we explored the influences of social support and co-rumination on suicidal behaviors using panel data set. We collected text data from 22,286 depressed users of a large ODC in China from March 2020 to July 2021, and conducted text mining and econometrics analyses to test our research questions. The results showed that depression users' online ruminative behaviors had a positive relationship with the co-rumination and had a negative relationship with social support received. Besides, constructive distractive behaviors (i.e., providing social support to others) increased the support users received from others but had a negative relationship with co-rumination. Depression users' future suicidal behaviors are influenced by past received social support and co-rumination. The received social supports and co-rumination have a negative and positive influence on depression users' future suicidal behaviors, respectively. Our results enrich the application of response style theory in online medicine. They provide meaningful insights into behaviors that influence the acquisition of online social support and the incidence of online co-rumination in ODCs. This study helps relevant institutions to conduct more targeted online suicide interventions for depression patients. © 2022 Elsevier Ltd

Designing for the post-pandemic era: Trends, focuses, and strategies learned from architectural competitions based on a text analysis.

The COVID-19 pandemic has made the built environment an important source of prevention and control, architects and scholars have thus been seeking countermeasures since the beginning of the outbreak. As design and construction cycles are long, only a few completed cases and evidence-based studies are available for reference. However, massive architectural competition works have emerged, which always been the soil for discussion and practice of cutting-edge design issues. These contain a vast number of ideas for solutions from various design dimensions-including cities, buildings, and facilities-and provide a great deal of materials worth analyzing and summarizing. Therefore, the exploration of competitions will provide us with public health intervention directions, strategies and a rethinking of the built environment. Using a text-mining approach, we analyzed 558 winning entries in architectural competitions related to the pandemic response, exploring specific issues, populations involved, coping strategies, and trends that emerged as the pandemic evolved. Our results show that the strategies proposed can be grouped into 17 keywords, with modularization being the most frequent strategy and related strategies like rapid assembly, flexible space, etc. are also took a significant percentage of the use. Further, we explored the technical orientation, year, territory, target groups, and target problems of the works which lead to a series of cross-comparison relationships. The results indicate that indirect impacts caused by the pandemic gained more attention and flexible Solutions were used more often highlighted the consensus when adapting to the uncertainties. The focus on the spiritual dimension is increasing year by year reflected the spiritual influences were gaining traction and the indirect impacts gradually showed up over time. The research will provide a strategy reference for the design response to the pandemic, as well as help understand the influence and significance of social factors behind the divergence of issue focuses and strategic tendency in different regions and times.

An Efficient Approach to Validate COVID-19 Related Vaccine Myths Utilizing LDA Algorithm

Scientists from the whole world have been working their heart and soul to invent the COVID-19 vaccine. When they are succeed to make the vaccine, various rumors are spread. COVID-19 situation has made our world standstill. When the vaccine came out for the first time, people were enthusiastic to take a shot. But the myth, rumors about vaccination also followed the success. In this paper, we have tried to validate the COVID-19 related vaccine myth and rumors with the help of the LDA algorithm. We have used data mining, text mining and sentiment analysis for the experiment. The outcome of our experiment has shown that most people are positive about vaccination but the negative impact is also there. Our experiment has found that most of the people are talking about "vaccine", "people","moron"and "ever". We have proposed a technique to validate this kind of vaccine myth. LDA algorithms have been able to predict and validate the myth up to 70% compared to other frameworks out there. Promising efficiency is exhibited by our experimental result. © 2022 ACM.

＜Editors' Choice＞ Developing online lectures using text mining reduces health workers' anxiety in non-epicenter areas of COVID-19.

COVID-19 is indirectly associated with various mental disorders such as anxiety, insomnia, and depression, and healthcare professionals who treat COVID-19 patients are particularly prone to severe anxiety. However, neither the anxiety of healthcare workers in non-epicenter areas nor the effects of knowledge support have been examined thus far. Participants were 458 staff working at the Toyota Regional Medical Center who completed a preliminary questionnaire of their knowledge and anxiety regarding COVID-19. Based on text mining of the questionnaire responses, participants were offered an online lecture. The effect of the lecture was analyzed using a pre- and post-lecture rating of anxiety and knowledge confidence, and quantitative text mining. The response rates were 45.6% pre- and 62.9% post-lecture. Open-ended responses regarding anxiety and knowledge were classified into seven clusters using a co-occurrence network. Before the lecture, 28.2%, 27.2%, and 20.3% of participants were interested in and anxious about "infection prevention and our hospital's response," "infection and impact on myself, family, and neighbors," and "general knowledge of COVID-19," respectively. As a result of the lecture, Likert-scale ratings for anxiety of COVID-19 decreased significantly and knowledge confidence increased significantly. These changes were confirmed by analyses of open-ended responses about anxiety, lifestyle changes, and knowledge. Positive changes were strongly linked to the topics focused on in the lecture, especially infection prevention. The anxieties about COVID-19 of healthcare workers in non-epicenter areas can be effectively reduced through questionnaire surveys and online lectures using text mining.

Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis.

BACKGROUND: The 2019 Novel Coronavirus (COVID-19) is responsible for thousands of deaths and hospitalizations. To curb the spread of this highly transmissible disease, governments enacted protective guidelines for its citizens, including social distancing and stay-at-home orders. These restrictions on social interactions can be especially problematic for individuals managing or recovering from addiction given that treatment often involves access to services and resources that became limited or even unavailable at this time. Social media sites like Twitter serve as a space for users to post questions and concerns about timely topics and allow for researchers to track common themes among the public. The goal of this study was to identify how the public was discussing addiction on Twitter during the COVID pandemic. Methods: We performed a text mining analysis to analyze tweets that contained "addiction" and "covid" to capture posts from the public that illustrated comments and concerns about addiction during the COVID-19 pandemic. We report on 3,301 tweets captured between January 31 and April 23, 2020. The study was conducted in the United States, but contained tweets from multiple countries. Results: The most prevalent topics had to do with services offered by Acadia Healthcare and Serenity Healthcare Centers, attempts to manage time while home, difficulties of coping with alcoholism amidst rising sales of alcohol, and attention to ongoing health crises (e.g.,., opioids, vaping). Additional topics included affordable telehealth services, research from France on the relationship between nicotine and COVID-19, concerns about gambling addiction, and changing patterns in substance misuse as drug availability varies. Conclusions: Analyzing Twitter content enables health professionals to identify the public's concerns about addiction during the COVID-19 pandemic. Findings from text mining studies addressing timely health topics can serve as preliminary analyses for building more comprehensive models, which can then be used to generate recommendations for the larger public and inform policy.