Problematic Use of Digital Technologies and Its Impact on Mental Health During COVID-19 Pandemic: Assessment Using Machine Learning

Abstract

Many nations have imposed lockdowns due to the COVID-19 pandemic as a measure to prevent the spread of disease among its population. These lockdowns have confined people at their homes which is leading them to use digital technologies such as Internet, social media, smartphones, more than ever before. The problematic use of these digital technologies may impact their mental and emotional health. This chapter discusses the role of machine learning to assess addiction to various digital technologies and its impact on mental and emotion health and on sleep quality during the COVID-19 pandemic. Three case studies are provided to demonstrate how machine learning can be used to assess these addictions and related disorders during the pandemic. Gaussian mixture clustering is implemented to group people with similar Twitter usage patterns to identify addictive Twitter usage during the pandemic. The results convey that 11.71% of users show addictive Twitter usage patterns and 4.05% of users show highly addictive Twitter usage patterns while 2.70% of users show dangerously addictive usage patterns. “Sadness” and “anger” are the dominating emotions among these users in contrast to “happiness” which is the dominating emotion among non-addictive users. A similar approach is used to cluster students with similar smartphone usage patterns and nomophobia scores to identify nomophobic behavior during the pandemic. The results show that 4.5% of students are at extremely high risk whereas 73% of students are at high risk. A review of studies identifies the emergence of machine learning for assessment of mental and emotional health during the COVID-19 pandemic. A case study on sleep quality assessment using data from wearable sensors convey that sleep quality of students has been reduced significantly during the pandemic with a maximum decrease of 90.90%.