Affiliation:
1. Edge Hill University, UK
Abstract
Social media platforms are widely used to share opinions, facts, and real-time general information on specific events. This chapter will focus on discussing and presenting data analytics approaches which combine a variety of techniques based on text mining, machine learning, network analysis, and mathematical modelling to assess real-time data extracted from social media and other suitable data related to pandemic outbreaks. The use of real-time insights regarding pandemic outbreaks provides a valuable tool to inform and validate existing modelling techniques and methods. Furthermore, this would also support the discovering process of actionable information to facilitate the decision-making process by enhancing the most informed and appropriate decision, based on the available data. The chapter will also focus on the visualisation and usability of the insight identified during the process to address a non-technical audience.
Reference35 articles.
1. Using online social networks to track a pandemic: A systematic review
2. Baker, J. E. (1987, July). Reducing bias and inefficiency in the selection algorithm. In Proceedings of the second international conference on genetic algorithms (Vol. 206, pp. 14-21). Academic Press.
3. Latent dirichlet distribution.;D. M.Blei;Journal of Machine Learning Research,2003
4. Booker, L. (1987). Improving search in genetic algorithms. Genetic Algorithms and Simulated Annealing, 61-73.