WHO Digital Intelligence Analysis for Tracking Narratives and Information Voids in the COVID-19 Infodemic

Purnat, Tina D; Vacca, Paolo; Burzo, Stefano; Zecchin, Tim; Wright, Amy; Briand, Sylvie; Nguyen, Tim

doi:10.3233/shti210326

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…Data are categorized as a public health taxonomy developed by experts to enable social understanding of COVID-19 pandemic narratives on social media platforms. This taxonomy was informed by previous work [ 12 , 13 ] but developed bespoke for this project and continually reviewed and revised. The taxonomy has 5 main global topics and 41 different categories.…”

Section: Methodsmentioning

confidence: 99%

“…The WHO has previously reported on using artificial intelligence (AI)–driven social listening to deliver actionable infodemic insights [ 9 ] and on the development and validation of a public health social listening taxonomy [ 12 ]. The COVID-19 public health taxonomy was designed to provide a practical and structured approach to identifying narratives shared on digital media [ 13 ], and taxonomy-driven data analysis and integration have since been applied to other outbreaks, such as mpox [ 14 ]. Developed by public health and digital health experts, the taxonomy enables data to be filtered into categories, allowing for the identification of where the global conversation is growing and what the information voids or issues of concern may be.…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study

White¹,

Gombert²,

Nguyen³

et al. 2023

JMIR Infodemiology

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study

White¹,

Gombert²,

Nguyen³

et al. 2023

JMIR Infodemiology

Self Cite

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“…Participation can be active, for example acting as "citizen scientists" (Silvertown, 2009) or a mass monitoring system (e.g., Zoe Health Study; Birkin, Vasileiou, & Stagg, 2021). The public can also passively inform scientists through their collective online discourse: "social listening" has enabled science communicators to tackle misinformation outbreaks by targeting information provision to the public's needs (Purnat et al, 2021;World Health Organization, 2021).…”

Section: Increasing Public Participationmentioning

confidence: 99%

Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation

Holford

Fasce

Tapper

et al. 2023

Science Communication

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Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.

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Section: Increasing Public Participationmentioning

confidence: 99%

Science Communication as a Collective Intelligence Endeavour: A Manifesto and Examples for Implementation

Holford¹,

Fasce²,

Tapper³

et al. 2022

Preprint

View full text Add to dashboard Cite

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue for the need for a new programme of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (1) a wider base of aggregated knowledge, (2) contributions from a diverse scientific community, (3) participatory input from stakeholders, and (4) better responsiveness to ongoing changes in the state of knowledge.

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WHO Digital Intelligence Analysis for Tracking Narratives and Information Voids in the COVID-19 Infodemic

Cited by 9 publications

References 6 publications

Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study

Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study

Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation

Science Communication as a Collective Intelligence Endeavour: A Manifesto and Examples for Implementation

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