The Relationships between Gender, Life Satisfaction, Loneliness and Problematic Internet Use during COVID-19: Does the Lockdown Matter?

In response to the COVID-19 pandemic, many governments have attempted to reduce virus transmission by implementing lockdown procedures, leading to increased social isolation and a new reliance on technology and the internet for work and social communication. We examined people's experiences working from home in the UK to identify risk factors of problematic internet use during the first lockdown period, specifically looking at life satisfaction, loneliness, and gender. A total of 299 adults completed the Problematic Internet Use Questionnaire-Short-Form-6, UCLA-3 Item Loneliness Scale, and Satisfaction with Life Scale online. Through structural equation modelling, we found that loneliness positively predicted problematic internet use while gender had no effect. Life satisfaction and age positively predicted loneliness but had no direct effect on problematic internet use, suggesting loneliness fully mediated their relationship with problematic internet use. Our study serves as a benchmark study of problematic internet use among those working from home during lockdown conditions, which may be utilized by future researchers exploring longitudinal patterns post-pandemic.

R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification.

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.