Using Natural Language Processing to Extract Intelligence for Public Health Crises

Abstract Background: The COVID-19 pandemic is an awakening call for public health agencies. Digital technologies create a decentralized information environment in which public health agencies must compete for audience attention and win their trust. Trust is a result of inclusiveness of all stakeholders, mutual understanding, and recognition of different perspectives. Thereby, public health agencies should apply two-way communication and cognitive empathy, by listening to local communities. Technology advancement in Artificial Intelligence has made it possible to “listen” to many stakeholders on social media. This study urges a focus on listening at local levels, for example, cities, given the abundance of geo-marked data, and the importance of community-level operations to manage public health crises.Methods: The case study presented combined AI methods with textual analysis and examined 180,128 tweets posted by four cities with large populations of people of color. Results: The findings discovered sentiment around “COVID Vaccines,” “Politics,” “Mitigation Measures,” and “Community/Local Issues” and critical moments of emotional changes.Conclusions: Our major contribution is to explain the motivation and the methods of extracting intelligence for the purpose of enhancing public trust in health agencies during crises.