A Discourse-Aware, Community-Informed Toolkit to Predict Virality and Impact of Vaccine Misinformation Contents

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Researchers aimed to “develop a contextually grounded natural language processing (NLP) model and build a toolkit around it to leverage the benefit of latest developments in data analytics, network simulations and NLP for increasing vaccine acceptance, vaccination rates and disseminating accurate scientific messages according to community needs.

Our final product, VIMP, will be a discourse-aware, community-informed toolkit to predict the virality and impact of vaccine misinformation contents. We will use a variety of contextually grounded NLP techniques to design our prediction models and will leverage the benefit of advanced simulation techniques for model training and evaluation. Our team will also engage with a diverse panel of community partners who provide advice on model development and tool building. CDC and its partners could use the tool to rapidly respond to misinformation about vaccines. The model and tool would further increase CDC’s and its partners’ ability to prioritize which misinformation content to respond to and how to allocate resources at the community level.”

Commentary:
To these people, there is only one possible explanation for the American public's declining faith in vaccines and skepticism of public health: misinformation. Instead of investigating why people don't trust its advice, the CDC awarded this $1 million grant leveraging natural language processing (NLP) modeling to monitor 'misinformation' and counteract it with government-approved messaging.

About the award

February 1, 2024 - January 31, 2026

Project information

Academia

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