Background: Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required. The World Health Organization Early Artificial Intelligence-Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges.
Objective: This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study.
Methods: Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning-based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables.
Results: The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use.
Conclusions: The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals.
Keywords: AI text analytics; COVID-19; artificial intelligence; infodemic; information voids; machine learning; narrative analysis; natural language processing; pandemic preparedness; pandemic response; public health; sentiment; social listening; social media.
©Becky K White, Arnault Gombert, Tim Nguyen, Brian Yau, Atsuyoshi Ishizumi, Laura Kirchner, Alicia León, Harry Wilson, Giovanna Jaramillo-Gutierrez, Jesus Cerquides, Marcelo D’Agostino, Cristiana Salvi, Ravi Shankar Sreenath, Kimberly Rambaud, Dalia Samhouri, Sylvie Briand, Tina D Purnat. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 21.08.2023.