Google Trends™ Dynamics for the Guidance of Open-Source Intelligence: Augmentation of Social-Media and Survey Surveillance of Population Mental Health

The unfolding of the COVID-19 outbreak was an unprecedented and unanticipated opportunity to understand how a sudden global shock modulates people's online searches when seeking information about their emotional well-being. It has also illustrated how public health surveillance systems were essential for tracking diseases' spatial and temporal dynamics and shaping rapid public policy changes. The present paper validates a data mining and processing framework which examines how digital epidemiology and machine learning reveal trends in human mental health and psychological distress expression variability. We present results obtained in two research exploring the relationship between Google Trends time-series in the digital surveillance of search engines during the pandemic and a selection of social media feeds and official UK well-being surveys. The generated body of evidence shows how data science can provide robust, finely grained, and replicable evidence on mental health measures at the population level. In the future, the digital surveillance analytics validated here can be rapidly deployed for crisis management and allow early detection of distress signals to better manage communication and policy action at population level.