Network Approach to Linguistic Pattern Analysis for User Gender Identification of Social Internet Services

Gender is a determinant of online social behavior, and it is important to understand gender dynamics in online spaces. This study examines gender identification as a conceptual framework for analyzing the content of social internet services and explores the complex dynamics of social internet services, focusing on the significant influence of gender identification on digital interactions. By integrating gender analysis, this research aims to comprehensively understand user behavior, communication patterns, and intent in social internet services. The research provides insights into the role of gender in online discourse, contributes to the understanding of how spatial cues and gender dynamics shape communication in social internet services and provides insights into online interaction patterns. This paper focuses on analyzing gendered communication patterns through the lens of network models and descriptive statistics. The study examines two representations of network models—activity-arc and activity-vertex—and uses Python libraries for data analysis. The data set, divided into control and experimental groups, is numerically scaled, and descriptive statistics reveal remarkable insights. Examination of linguistic characteristics reveals distinct differences between control groups F and M of users of social internet services. The study applies a matrix subtraction technique and uses network models to analyze communication characteristics across gender groups of users of social internet services. The results indicate differences in linguistic patterns between male and female groups of users of social internet services and represent visualizations and descriptive statistics.