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In a rapidly changing public health crisis such as COVID-19, researchers need innovative approaches that can effectively link qualitative approaches and computational methods. In this article, computational and qualitative methods are used to analyze survey data collected in March 2020 ( n = 2,270) to explore the content of persuasive messages and their relationship with self-reported health behavior—that is, social distancing. Results suggest that persuasive messages, based on participants’ perspectives, vary by gender and race and are associated with self-reported health behavior. This article illustrates how qualitative analysis and structural topic modeling can be used in synergy in a public health study to understand the public’s perception and behavior related to science issues. Implications for health communication and future research are discussed.

Public discourse and deliberation are key to developing socially responsible and acceptable human gene editing research and applications. Researchers have raised concerns, however, that discourse a...

Contains fulltext : 240024.pdf (Publisher’s version ) (Open Access) Objective: To investigate the applicability of supervised machine learning (SML) to classify health-related webpages as 'reliable' or 'unreliable' in an automated way. Methods: We collected the textual content of 468 different Dutch webpages about early childhood vaccination. Webpages were manually coded as 'reliable' or 'unreliable' based on their alignment with evidence-based vaccination guidelines. Four SML models were trained on part of the data, whereas the remaining data was used for model testing. Results: All models appeared to be successful in the automated identification of unreliable (F1 scores: 0.54–0.86) and reliable information (F1 scores: 0.82–0.91). Typical words for unreliable information are 'dr', 'immune system', and 'vaccine damage', whereas 'measles', 'child', and 'immunization rate', were frequent in reliable information. Our best performing model was also successful in terms of out-of-sample prediction, tested on a dataset about HPV vaccination. Conclusion: Automated classification of online content in terms of reliability, using basic classifiers, performs well and is particularly useful to identify reliable information. Practice implications The classifiers can be used as a starting point to develop more complex classifiers, but also warning tools which can help people evaluate the content they encounter online. 7 p.