Evolutionary clustering and community detection algorithms for social media health surveillance.

The prominent rise of social networks within the past decade have become a gold mine for data mining operations seeking to model the real world through these virtual worlds. One of the most important applications that has been proposed is utilizing information generated from social networks as a supplemental health surveillance system to monitor disease epidemics. At the time this research was conducted in 2020, the COVID-19 virus had evolved into a global pandemic, forcing many countries to implement preventative measures to halt its expanse. Health surveillance has been a powerful tool in placing further preventative measures, however it is not a perfect system, and slowly collected, misidentified information can prove detrimental to these efforts. This research proposes a new potential surveillance avenue through unsupervised machine learning using dynamic, evolutionary variants of clustering algorithms DBSCAN and the Louvain method to allow for community detection in temporal networks. This technique is paired with geographical data collected directly from the social media Twitter, to create an effective and accurate health surveillance system that grows as time passes. The experimental results show that the proposed system is promising and has the potential to be an advancement on current machine learning health surveillance techniques.