Temporal patterns and variations of depression-related online search behavior: A longitudinal analysis of Google search data

Abstract The current research work represents the result of analyzing online health information-seeking behavior regarding depression over ten years 2013–2022 in Finland, finding the rhythms and fluctuations on different time-scales. Our paper analyzes diurnal, weekly, and seasonal patterns in six Google-suggested depression-related search queries by relying on data provided by Google Trends and the Google Health Trends Application Programming Interface-GHT-API. Our results reveal that search volumes follows temporal patterns on daily, weekly, and seasonal levels. We observe a periodical robust diurnal pattern with a significant nightly peak. Weekly patterns highlight increased search activity during weekends, with the exception of the term "Masennus"(eng. depression) which exhibits unique dynamics during weekdays. Seasonal patterns take the shape of bimodal curves for "masennus" and "masennustesti" with a peak in search interest during spring and autumn and deep during summer. These findings bear implications for healthcare providers and policy makers in pursuit of the best mental health care. Driving forces for such temporal patterns may provide the basis for further research, thus giving more depth to our understanding of online information seeking in mental health contexts.