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Using Machine Learning Technology (Early Artificial Intelligence-Supported Response With Social Listening Platform)

Becky K White1, Arnault Gombert2, Tim Nguyen1

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Area of Science:

  • Public Health Informatics
  • Computational Social Science
  • Epidemiology

Background:

  • The COVID-19 pandemic highlighted the need for rapid social understanding to manage infodemics.
  • Traditional social media analysis platforms, designed for commercial use, are underutilized for public health insights.
  • New tools are required to overcome challenges in applying social listening to public health surveillance.

Purpose of the Study:

  • To describe the development of the World Health Organization Early Artificial Intelligence-Supported Response with Social Listening (EARS) platform.
  • To detail the data sourcing, machine learning model development, and validation processes.
  • To present findings from the EARS platform's pilot study.

Main Methods:

  • Daily collection of web-based conversations in 9 languages.
  • Development of a public health taxonomy with 5 main categories and 41 subcategories for COVID-19 narratives.
  • Application of a semisupervised machine learning algorithm and comparison with Boolean search filters for validation (measuring recall and precision).

Main Results:

  • The EARS platform was successfully developed, validated, and used to analyze COVID-19 conversations from December 2020 to February 2022.
  • The machine learning algorithm demonstrated superior precision and recall compared to Boolean search filters in English and Spanish (P<.001).
  • Demographic filters provided valuable insights, with user gender distribution aligning with general social media usage patterns.

Conclusions:

  • The EARS platform effectively addresses public health analysts' needs during health crises like the COVID-19 pandemic.
  • Integrating a public health taxonomy with AI in a user-friendly social listening tool enhances understanding of global narratives.
  • Machine learning offers a more accurate and scalable approach than keyword-based methods for analyzing digital social data during infodemics.