Model, Analyze, and Comprehend User Interactions within a Social Media Platform
Md Kaykobad Reza, S M Maksudul Alam, Yiran Luo, Youzhe Liu, Md Siam
TL;DR
We address understanding user interactions on a social media platform by constructing a directed user interaction graph based on post-comment relationships. We build the UIG from r/ucr data (Jan 2022–Dec 2023), edges from the comment author to the post/comment authors, including immediate-ancestor context and self-loops, and identify clusters via SCCs with WC/CTUP/SC definitions. Key findings include that a sizable share of active users form strong connections, a small fraction drive dynamics, and activity correlates with popularity, with content skewing positive. The framework supports improved understanding and management of online communities, with potential applications in content recommendation, moderation, and community design.
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.