Model, Analyze, and Comprehend User Interactions within a Social Media Platform

TL;DR

This paper introduces a graph-based framework to analyze user interactions on social media, revealing community structure, user behavior patterns, and content preferences through data-driven insights.

Contribution

It presents a novel graph modeling approach for social media interactions, providing new insights into community dynamics and user behavior analysis.

Findings

56.05% of users are strongly connected within communities

0.8% of users significantly influence community dynamics

82.41% of users prefer positive and informative content

Abstract

In this study, we propose a novel graph-based approach to model, analyze and comprehend user interactions within a social media platform based on post-comment relationship. We construct a user interaction graph from social media data and analyze it to gain insights into community dynamics, user behavior, and content preferences. Our investigation reveals that while 56.05% of the active users are strongly connected within the community, only 0.8% of them significantly contribute to its dynamics. Moreover, we observe temporal variations in community activity, with certain periods experiencing heightened engagement. Additionally, our findings highlight a correlation between user activity and popularity showing that more active users are generally more popular. Alongside these, a preference for positive and informative content is also observed where 82.41% users preferred positive and informative content. Overall, our study provides a comprehensive framework for understanding and managing online communities, leveraging graph-based techniques to gain valuable insights into user behavior and community dynamics.