Understanding and Analyzing Social Network Structure Among University Students

Abstract

Mobile phone arguably is one of the most reached and used technology in human history. Technology has become ubiquitous in the life of human beings. Equipped with multiple sensors and devices, smartphones can record each and every action, psychological and environmental states of users, making it a goldmine of rich data about and insight into the dynamics of human communication, human behavior, relationships, and social interaction. As a source of data for empirical research, this device has gotten much attention from scholars in various disciplines like sociology, social psychology, urban studies, communication and media studies, public health, epidemiology, and computer science. This research tries to understand the structure of social networks of university students by investigating their communication patterns using self-reported mobile phone data. We collected behavioral data for one month using a Call Log Analytics mobile phone app. The data contained information about respondents’ contacts, date and time of call, duration of the call, call type (e.g., incoming, outgoing, missed), and frequency of the call. We used UCINET to analyze the data. In this investigation, we can find those students who are connected to most of the classmates and maintain a strong relationship and perform a task successfully using the values of eigenvector, closeness, and betweenness centrality, respectively. Moreover, this study also helps us to find out the pattern of the students using contact duration, incoming and outgoing calls.