Analyzing the Impact of Online Social Networks on Social Behavior of Students Using Convolutional Neural Networks"

Abstract

The use of Online Social Networks (OSNs) is continuously increasing especially in students, which produces a major impact in students’ educational performance, their interaction, and their social bonding. And this makes it a critical area of study in understanding their social behavior. Though various studies have already been conducted, there is still a gap in quantitative assessment of the social behavior among the students, specifically using advanced computational techniques. The aim of this study is to assess the impact of OSNs on students’ social behavior, such as social bonding, interactions, and emotional quotient (EQ), by using Convolutional Neural Network (CNN). This study uses CNNs to examine the students’ behavioral patterns and correlations in a dataset that is derived from the online activities on OSNs. The results show the significant impact of OSNs in framing the students’ social behavior, with both positive and negative outcomes. The results help to understand the digital socialization practices, which may lead to form strategies for balanced usage of OSNs among students. This study is crucial for psychologists, policymakers, and educators to cope up with the challenges raised by rapid growth in using OSNs.

Keywords- Online Social Networks, Social Behavior, Students & Neural Networks