Analysis of Bipartite Networks in Anime Series: Textual Analysis, Topic Clustering, and Modeling

TL;DR

This paper investigates how textual descriptions of anime influence user community formation in bipartite networks, introducing a novel variable based on word cluster frequency to analyze community dynamics and network structure.

Contribution

It presents a new variable derived from bigram analysis to quantify the impact of textual content on community cohesion in anime user networks.

Findings

Textual content significantly affects community structure.

Word cluster frequency correlates with community cohesion.

Implications for improving recommendation systems.

Abstract

This article analyzes a specific bipartite network that shows the relationships between users and anime, examining how the descriptions of anime influence the formation of user communities. In particular, we introduce a new variable that quantifies the frequency with which words from a description appear in specific word clusters. These clusters are generated from a bigram analysis derived from all descriptions in the database. This approach fully characterizes the dynamics of these communities and shows how textual content affect the cohesion and structure of the social network among anime enthusiasts. Our findings suggest that there may be significant implications for the design of recommendation systems and the enhancement of user experience on anime platforms.