Abstract
A large-scale analysis of diurnal and seasonal mood cycles in global social networks has been performed successfully over the past ten years using Twitter, Facebook and blogs. This study describes the application of remote biometric technologies to such investigations on a large scale for the first time. The performance of this research was under real conditions producing results that conform to natural human diurnal and seasonal rhythm patterns. The derived results of this, 208 million data research on diurnal emotions, valence and facial temperature correlate with the results of an analogical Twitter research performed worldwide (UK, Australia, US, Canada, Latin America, North America, Europe, Oceania, and Asia). It is established that diurnal valence and sadness were correlated with one another both prior to and during the period of the coronavirus crisis, and that there are statistically significant relationships between the values of diurnal happiness, sadness, valence and facial temperature and the numbers of their data. Results from the simulation and formal comparisons appear in this article. Additionally the analyses on the COVID-19 screening, diagnosing, monitoring and analyzing by applying biometric and AI technologies are described in Housing COVID-19 Video Neuroanalytics.
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