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
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…
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Taxonomy
TopicsKnowledge Management and Sharing · Advanced Text Analysis Techniques · Impact of Technology on Adolescents
