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  1. Fulltext Content framing role on public sentiment formation for pre-crisis detection on sensitive issue via sentiment analysis and content analysis
    Watimin NH, Zanuddin H, Rahamad MS, Yadegaridehkordi E
    PLoS One, 2023;18(10):e0287367.
    PMID: 37851696 DOI: 10.1371/journal.pone.0287367
    Social media has been tremendously used worldwide for a variety of purposes. Therefore, engagement activities such as comments have attracted many scholars due its ability to reveal many critical findings, such as the role of users' sentiment. However, there is a lacuna on how to detect crisis based on users' sentiment through comments, and for such, we explore framing theory in the study herein to determine users' sentiment in predicting crisis. Generic content framing theory consists of conflict, economic, human interest, morality, and responsibility attributes frame as independent variables whilst sentiment as dependent variables. Comments from selected Facebook posting case studies were extracted and analysed using sentiment analysis via Application Programme Interface (API) webtool. The comments were then further analysed using content analysis via Positive and Negative Affect Schedule (PANAS) scale and statistically evaluated using SEM-PLS. Model shows that 44.8% of emotion and reactions towards sensitive issue posting are influenced by independent variables. Only economic consequences and responsibility attributes frame had correlation towards emotion and reaction at p<0.05. News reporting on direction towards economic and responsibility attributes sparks negative sentiment, which proves that it can best be described as pre-crisis detection to assist the Royal Malaysian Police and other relevant stakeholders to prevent criminal activities in their respective social media.
  2. Fulltext Evaluating COVID-19 infection prevention measures in Malaysia: A fuzzy DEMATEL approach
    Yadegaridehkordi E, Nilashi M, Nizam Bin Md Nasir MH, Safie Bin Mohd Satar N, Momtazi S
    Digit Health, 2023;9:20552076231211670.
    PMID: 38074341 DOI: 10.1177/20552076231211670
    OBJECTIVE: Since unexpected COVID-19 has been causing massive losses worldwide, preventive measures have been emergency provided to curb the expansion of the epidemic and cut off transmission routes. However, there is a lack of studies that comprehensively address COVID-19 infection prevention measures. This aims to provide a comprehensive evaluation framework to identify the factors impacting COVID-19 infection prevention. Meanwhile, categorizing factors into individual, social, environmental, and technological dimensions and uncovering their interrelationships and level of importance are indeed novelties of this study.

    METHODS: An integration of fuzzy logic and decision-making trial and evaluation laboratory (DEMATEL) is utilized, and data was collected from a panel of professional experts in Malaysia. Using a cause-effect relationship diagram, the fuzzy DEMATEL method evaluates the causal relationships between factors.

    RESULTS: Findings showed that environmental factors play the most significant roles in preventing COVID-19 infection, followed by technology, individual, and social factors. Getting vaccinated is the most crucial factor in the environmental dimension in cutting the spread of COVID-19. Telehealth, the use of personal protective equipments (PPEs), and the adoption of social distancing are the most important measures in technology, individual and social dimensions, respectively.

    CONCLUSIONS: This study offered valuable insights for policymakers and healthcare professionals in designing and implementing effective strategies to prevent pandemic disease transmission. Findings can be practically applied to optimize and prioritize infection prevention measures, assign resources more effectively, and guide evidence-based decision-making in the face of evolving pandemic situations. This process involves the active commitment of all parties, including governments, medical health executives, and citizens.

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