Model, Analyze, and Comprehend User Interactions and Various Attributes within a Social Media Platform
<|reference_start|>Model, Analyze, and Comprehend User Interactions and Various Attributes within a Social Media Platform: How can we effectively model, analyze, and comprehend user interactions and various attributes within a social media platform based on post-comment relationship? In this study, we propose a novel graph-based approach to model and analyze 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.<|reference_end|>
@article{reza2024model,,
    title={Model, Analyze, and Comprehend User Interactions and Various Attributes
  within a Social Media Platform},
    author={Md Kaykobad Reza, S M Maksudul Alam, Yiran Luo and Youzhe Liu},
    journal={arXiv preprint arXiv:2403.15937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15937},
    primaryClass={cs.SI cs.IR}
}