Abstract
Social media (SM) is establishing a new era of tools with multi-usage capabilities. Governments, businesses, organizations, as well as individuals are engaging in, implementing their promotions, sharing opinions and propagating decisions on SM. We need filters, validators and a way of weighting expressed opinions in order to regulate this continuous data stream. This chapter presents trends and attempts by the research community regarding: (a) the influence of SM on attitudes towards a specific domain, related to public health and safety (e.g. diseases, vaccines, mental health), (b) frameworks and tools for monitoring their evolution and (c) techniques for suggesting useful interventions for nudging public sentiment towards best practices. Based on the state of the art, we discuss and assess whether SM can be used as means of prejudice or esteem regarding online opinions on health care. We group the state of the art in the following categories: virus–illness outbreaks, anti-vaccination, mental health, social trends and food and environment. Furthermore, we give more weight to virus–illness outbreaks and the anti-vaccination issues/trends in order to examine disease outbreak prevention methodologies and vaccination/anti-vaccination incentives, whilst discussing their performance. The goal is to consolidate the state of the art and give well-supported directions for future work. To sum up, this chapter discusses the aforementioned concepts and related biases, elaborating on forecasting and prevention attempts using SM data.
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Koukaras, P., Rousidis, D., Tjortjis, C. (2020). Forecasting and Prevention Mechanisms Using Social Media in Health Care. In: Maglogiannis, I., Brahnam, S., Jain, L. (eds) Advanced Computational Intelligence in Healthcare-7. Studies in Computational Intelligence, vol 891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61114-2_8
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