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This study uses Bayesian networks to integrate survey, social network, and official statistics for well-being evaluation. Findings show social media data can potentially anticipate official statistics, offering timely insights.

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Area of Science:

  • Social Sciences
  • Data Science
  • Statistics

Background:

  • Traditional well-being assessments often rely on surveys and official statistics.
  • Social network data offers a novel, high-frequency source of information.
  • Integrating diverse data sources presents methodological challenges.

Purpose of the Study:

  • To develop a Bayesian network model for combining survey, social network, and official statistics.
  • To analyze the relationships between different data types in well-being assessment.
  • To investigate the predictive power of social media big data for official statistics.

Main Methods:

  • Bayesian network modeling was employed to integrate heterogeneous data.
  • Data sources included traditional surveys, social network data (Twitter), and official statistics (ISTAT).
  • Analysis covered provincial and regional levels with varied time frequencies (daily, quarterly, annual).

Main Results:

  • The Bayesian network successfully integrated data from diverse sources and granularities.
  • A significant relationship was identified between social network data and official statistics.
  • Social media data demonstrated potential in anticipating trends in official labor market statistics.

Conclusions:

  • Bayesian networks provide a flexible framework for multi-source data integration in well-being studies.
  • Social media data can serve as a valuable, timely complement to traditional statistical indicators.
  • This approach enhances the timeliness and scope of well-being assessments.