Model, Analyze, and Comprehend User Interactions within a Social Media Platform
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.
Cite
@article{arxiv.2403.15937,
title = {Model, Analyze, and Comprehend User Interactions within a Social Media Platform},
author = {Md Kaykobad Reza and S M Maksudul Alam and Yiran Luo and Youzhe Liu and Md Siam},
journal= {arXiv preprint arXiv:2403.15937},
year = {2024}
}
Comments
Accepted by 27th International Conference on Computer and Information Technology (ICCIT), 2024. 6 Pages, 6 Figures