Model, Analyze, and Comprehend User Interactions within a Social Media Platform
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912135589658624 |
|---|---|
| author | Reza, Md Kaykobad Alam, S M Maksudul Luo, Yiran Liu, Youzhe Siam, Md |
| author_facet | Reza, Md Kaykobad Alam, S M Maksudul Luo, Yiran Liu, Youzhe Siam, Md |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15937 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model, Analyze, and Comprehend User Interactions within a Social Media Platform Reza, Md Kaykobad Alam, S M Maksudul Luo, Yiran Liu, Youzhe Siam, Md Social and Information Networks Information Retrieval 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. |
| title | Model, Analyze, and Comprehend User Interactions within a Social Media Platform |
| topic | Social and Information Networks Information Retrieval |
| url | https://arxiv.org/abs/2403.15937 |