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Activeness and Loyalty Analysis in Event-Based Social Networks.

Thanh Trinh1, Dingming Wu1, Joshua Zhexue Huang1

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Summary
This summary is machine-generated.

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.

Keywords:
EBSNsactivenessloyaltysocial networks

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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.