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Activeness and Loyalty Analysis in Event-Based Social Networks.
Thanh Trinh1, Dingming Wu1, Joshua Zhexue Huang1
1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
This study introduces methods to measure group activeness and user loyalty in event-based social networks (EBSNs). Identifying key features improves the prediction of social group dynamics and user engagement.
Area of Science:
- Social Network Analysis
- Computer Science
Background:
- Event-based social networks (EBSNs) facilitate online group formation and offline event organization.
- Group activeness and user loyalty are critical for understanding the growth and sustainability of these online communities.
- Existing research has limited focus on quantifying these dynamics within EBSNs.
Purpose of the Study:
- To define and measure group activeness and user loyalty in the context of EBSNs.
- To develop a novel method for assessing the dynamics of social groups within these platforms.
- To identify key features that predict group activeness and user loyalty.
Main Methods:
- Analysis of EBSN structures and feature generation from crawled datasets.
- Definition of group activeness and user loyalty using time-window-based metrics.
- Development of an association matrix for group activeness labeling.
- Measurement of user loyalty based on event attendance within time windows.
- Application of machine learning techniques to validate labels and identify predictive features.
Main Results:
- A proposed method effectively measures group activeness by analyzing event frequency ratios between time windows.
- User loyalty is quantified based on attended events and integrated as a feature for group activeness.
- Machine learning models successfully verified activeness labels.
- A subset of highly correlated features significantly improved prediction accuracy compared to using all features.
Conclusions:
- The study provides a robust framework for quantifying group activeness and user loyalty in EBSNs.
- The findings highlight the importance of specific features in predicting social group dynamics.
- The research offers valuable insights for understanding and potentially influencing the health of online social groups.
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