Model, Analyze, and Comprehend User Interactions within a Social Media Platform
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Characterizing User Archetypes and Discussions on Scored.co
In recent years, the proliferation of social platforms has drastically transformed the way individuals interact, organize, and share information. In this scenario, we experience an unprecedented increase in the scale and…
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content. This simulation combines LLM agents with…
MisinformationControl Strategies for Recommendation Systems in Social Networks
A closed-loop control model to analyze the impact of recommendation systems on opinion dynamics within social networks is introduced. The core contribution is the development and formalization of model-free and model-bas…
Recommendation SystemsLet’s Chat: Understanding User Expectations in Socialbot Interactions
This paper analyzes data from the 2021 Amazon Alexa Prize Socialbot Grand Challenge 4, in order to better understand the differences between human-computer interactions (HCI) in a socialbot setting and conventional human…
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social
Decentralized social media platforms like Bluesky Social (Bluesky) have made it possible to publicly disclose some user behaviors with millisecond-level precision. Embracing Bluesky's principles of open-source and open-d…
BlockingGraph Neural NetworkLearning Network RepresentationsNetwork Community Partition+2