Global Trends in Social Prescribing: Web-Based Crawling Approach.

BACKGROUND: Social loneliness is a prevalent issue in industrialized countries that can lead to adverse health outcomes, including a 26% increased risk of premature mortality, coronary heart disease, stroke, depression, cognitive impairment, and Alzheimer disease. The United Kingdom has implemented a strategy to address loneliness, including social prescribing-a health care model where physicians prescribe nonpharmacological interventions to tackle social loneliness. However, there is a need for evidence-based plans for global social prescribing dissemination. OBJECTIVE: This study aims to identify global trends in social prescribing from 2018. To this end, we intend to collect and analyze words related to social prescribing worldwide and evaluate various trends of related words by classifying the core areas of social prescribing. METHODS: Google's searchable data were collected to analyze web-based data related to social prescribing. With the help of web crawling, 3796 news items were collected for the 5-year period from 2018 to 2022. Key topics were selected to identify keywords for each major topic related to social prescribing. The topics were grouped into 4 categories, namely Healthy, Program, Governance, and Target, and keywords for each topic were selected thereafter. Text mining was used to determine the importance of words collected from new data. RESULTS: Word clouds were generated for words related to social prescribing, which collected 3796 words from Google News databases, including 128 in 2018, 432 in 2019, 566 in 2020, 748 in 2021, and 1922 in 2022, increasing nearly 15-fold between 2018 and 2022 (5 years). Words such as health, prescribing, and GPs (general practitioners) were the highest in terms of frequency in the list for all the years. Between 2020 and 2021, COVID, gardening, and UK were found to be highly related words. In 2022, NHS (National Health Service) and UK ranked high. This dissertation examines social prescribing-related term frequency and classification (2018-2022) in Healthy, Program, Governance, and Target categories. Key findings include increased "Healthy" terms from 2020, "gardening" prominence in "Program," "community" growth across categories, and "Target" term spikes in 2021. CONCLUSIONS: This study's discussion highlights four key aspects: (1) the "Healthy" category trends emphasize mental health, cancer, and sleep; (2) the "Program" category prioritizes gardening, community, home-schooling, and digital initiatives; (3) "Governance" underscores the significance of community resources in social prescribing implementation; and (4) "Target" focuses on 4 main groups: individuals with long-term conditions, low-level mental health issues, social isolation, or complex social needs impacting well-being. Social prescribing is gaining global acceptance and is becoming a global national policy, as the world is witnessing a sharp rise in the aging population, noncontagious diseases, and mental health problems. A successful and sustainable model of social prescribing can be achieved by introducing social prescribing schemes based on the understanding of roles and the impact of multisectoral partnerships.

Social Media vs. News Platforms: A Cross-analysis for Fake News Detection Using Web Scraping and NLP

With the widespread use of social media platforms within our modern society, these platforms have become a popular medium for disseminating news across the globe. While some of these platforms are considered reliable sources for sharing news, others publicize the information without much validation. The transmission of fake news on social media impacts people's behavior and negatively influences people's decisions. During the COVID-19 outbreak, it was more evident than ever. This has led to a demand for conducting research studies to explore sophisticated approaches to assess the integrity of news worldwide. The main objective of this research paper was to outline our proposed experimental methodology to detect and access fake news using Data Mining and Natural Language Processing. The presented research effort provides a method to verify the authenticity of the news disseminated in social networks by dividing the process into four significant stages: news aggregation, publication collection, data analysis, and matching results. © 2022 ACM.

COVID-19 Vaccine Perception in South Korea: Web Crawling Approach.

BACKGROUND: The US Centers for Disease Control and Prevention and the World Health Organization emphasized vaccination against COVID-19 because physical distancing proved inadequate to mitigate death, illness, and massive economic loss. OBJECTIVE: This study aimed to investigate Korean citizens' perceptions of vaccines by examining their views on COVID-19 vaccines, their positive and negative perceptions of each vaccine, and ways to enhance policies to increase vaccine acceptance. METHODS: This cross-sectional study analyzed posts on NAVER and Instagram to examine Korean citizens' perception of COVID-19 vaccines. The keywords searched were "vaccine," "AstraZeneca," and "Pfizer." In total 8100 posts in NAVER and 5291 posts in Instagram were sampled through web crawling. Morphology analysis was performed, overlapping or meaningless words were removed, sentiment analysis was implemented, and 3 public health professionals reviewed the results. RESULTS: The findings revealed a negative perception of COVID-19 vaccines; of the words crawled, the proportion of negative words for AstraZeneca was 71.0% (476/670) and for Pfizer was 56.3% (498/885). Among words crawled with "vaccine," "good" ranked first, with a frequency of 13.43% (312/2323). Meanwhile, "side effect" ranked highest, with a frequency of 29.2% (163/559) for "AstraZeneca," but 0.6% (4/673) for "Pfizer." With "vaccine," positive words were more frequently used, whereas with "AstraZeneca" and "Pfizer" negative words were prevalent. CONCLUSIONS: There is a negative perception of AstraZeneca and Pfizer vaccines in Korea, with 1 in 4 people refusing vaccination. To address this, accurate information needs to be shared about vaccines including AstraZeneca, and the experiences of those vaccinated. Furthermore, government communication about risk management is required to increase the AstraZeneca vaccination rate for herd immunity before the vaccine expires.

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis.

It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and "self-media," together contribute to the information spread of positive sentiment.