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This study introduces a dynamic multidimensional framework for analyzing social media data streams. It enhances public health surveillance by identifying relevant events, topics, and users in real-time.

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Area of Science:

  • Computational Social Science
  • Public Health Informatics
  • Data Science

Background:

  • Social network analysis increasingly uses user-generated data for public health surveillance (PHS).
  • Existing methods primarily analyze static social media datasets offline.
  • A dynamic, multidimensional framework for real-time social data analysis in PHS is lacking.

Purpose of the Study:

  • To propose a dynamic multidimensional approach for analyzing social data streams.
  • To develop methods for filtering relevant user-generated content for PHS.
  • To enable real-time identification and analysis of public health trends from social media.

Main Methods:

  • A dynamic multidimensional model for processing social data streams.
  • Unsupervised text mining to continuously update data dimensions.
  • Analysis of semantic and temporal patterns in social media posts.
  • Definition of quality metrics for filtering relevant user profiles and messages.

Main Results:

  • The approach effectively filters out out-of-domain and low-quality user data.
  • Specific user profiles are identifiable through their message content and descriptions.
  • The model successfully identifies key events and topics, analyzing audience and impact.

Conclusions:

  • The dynamic multidimensional model is effective for PHS applications.
  • It enables the identification and analysis of relevant public health events and topics from social media data.
  • The framework provides a valuable tool for real-time public health surveillance.