Assessment of Social Capital from Mobile Сommunication Data: A Cascade Model Based on Random Forest and Logistic Regression

With the rapid development of mobile technologies, analyzing data generated by mobile devices is becoming increasingly important. A wide range of applications, from marketing to healthcare, require the development of effective methods for extracting valuable information from this data. This study is devoted to developing a methodology for assessing an individual's social capital based on the analysis of mobile communication data. To assess social capital, we propose a two-stage Cascade Model that combines the advantages of the Random Forest (RF) and Logistic Regression (LR) algorithms. In the first stage, RF is used to select the most significant features reflecting various aspects of social capital. In the second stage, LR is used to assess of social capital, taking into account nonlinear relationships between features. The results of the study open up new opportunities for studying social phenomena and can be used in as sociology, marketing, and urban planning.