01/03/2023
The Association for Psychological Science (APS) asked Megan Birmingham (MB), a student of Dr. Valerie Reyna, who participated in the 2022 Annual Convention in Chicago to share her research, her findings, and the next steps in work relating to artificial intelligence (AI) and psychological science.
Qualitative and Quantitative Assessments of Topic Gists Extracted Using Machine Learning from Social Media Messages about COVID-19
Megan A. Birmingham, Valerie F. Reyna, Demetrius Bryson, and Sarah M. Edelson, Cornell University, and David A. Broniatowski, George Washington University
APS - What did the research reveal that you didn’t already know?
MB - We learned that millions of social media messages about important health topics related to COVID-19 could be summarized using machine learning models and that human judges could evaluate these models. Fuzzy-trace theory allowed us to meaningfully interpret all of this. We identified two “gist” themes that we examined in depth: (1) social distancing/lockdowns and (2) relationships between COVID-19 and the flu. Crucially, as anticipated by fuzzy-trace theory, the gists human judges took away from machine-generated topics were informed by personal experience with the pandemic and prior knowledge. Overall, we learned how people were thinking about and understanding social media messages, specifically at a critical stage of COVID-19—right when it was declared a pandemic.
APS - How might your findings contribute to the broader research on artificial intelligence and psychological science?
MB - We used machine learning algorithms that extracted the gist of millions of social media messages. Our project focused on two topics, but the research overall represents the first step in systematically evaluating these AI models. The two topics we focused on were related to risk reduction for COVID-19 and other viral outbreaks. We can use the knowledge gained here to aid in the larger effort of increasing automated gist elicitation. This would help public health communicators to quickly understand which gists should be communicated during outbreaks, how to communicate them, and which communities they should be communicated to.
APS - What are your next steps regarding this research?
MB - Next steps for this project include analyzing how gist messages evolve over time as perceptions of risks and benefits change. Fuzzy-trace theory will be helping us with that goal because it generates predictions about likely gist topics and about how people communicate about risks (Reyna, 2021, in Proceedings of the National Academy of Sciences, is open access and has details: https://www.pnas.org/doi/10.1073/pnas.1912441117). We hope to use this research to produce messages, with the help of AI systems, that promote risk-reduction behaviors and to improve public health communication about how to express the risks and benefits of behaviors. This research has the potential to empower individuals to make choices that not only mitigate the spread of COVID-19 but also address other public health emergencies in the future.
