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Network embedding aided vaccine skepticism detection.

Ferenc Béres1, Tamás Vilmos Michaletzky2, Rita Csoma2

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This summary is machine-generated.

This study shows network effects significantly improve detecting COVID vaccine skepticism on Twitter. Analyzing Twitter data reveals network information enhances classification accuracy for vaccination attitudes.

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Area of Science:

  • Computational Social Science
  • Public Health Informatics
  • Social Network Analysis

Background:

  • Vaccine skepticism presents a long-standing public health challenge, amplified by the COVID-19 pandemic.
  • Understanding and combating misinformation regarding COVID-19 vaccination is critical.

Purpose of the Study:

  • To investigate automatic methods for assessing COVID-19 vaccination views on Twitter.
  • To demonstrate the impact of network effects on classifying vaccination skeptic content.

Main Methods:

  • Collected and manually labeled vaccination-related Twitter content from early 2021.
  • Employed network embedding algorithms (e.g., Walklets) combined with text embedding.
  • Developed classifiers to analyze vaccination attitudes based on Twitter content and network structure.

Main Results:

  • Network information significantly improves the accuracy of classifying vaccination attitudes compared to content-based methods alone.
  • The integration of network embedding algorithms enhanced the performance of classifiers.
  • Walklets algorithm notably improved the Area Under the Curve (AUC) of the best classifier.

Conclusions:

  • Network effects are crucial for accurately detecting vaccination skeptic content on social media.
  • Automatic methods leveraging both content and network data offer a promising approach to monitor public health discourse.
  • The study's findings and resources (labels, code) are publicly available to facilitate further research.