Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jul 31, 2020
Date Accepted: May 12, 2021
Framing Vaccines among the Public on Social Media: Developing Machine Learning Algorithms for Detecting and Visualizing Pro and Anti-Vaccine Discourse
ABSTRACT
Background:
Exposure to anti-vaccine content on social media has been associated with delays and refusals of vaccinations, while pro-vaccine campaigns devised to disseminate the preventive benefits of vaccines have not succeeded in increasing vaccine uptake rates. Reasons remain unknown why anti-vaccine messaging hampers uptake while pro-vaccine campaigns do not improve it.
Objective:
We aim to identify reasons for the disparate effectiveness of anti- versus pro-vaccine social media content on vaccine delivery rates. In so doing, we apply the perspectives of message framing used in interpersonal health communication to explain why individuals refuse to adopt preventive behaviors. Specifically, we compare (1) the diversity, coherence, and distinctiveness of topics discussed by pro- and anti-vaccine communities and (2) message frames used to portray vaccines as a public health accomplishment or harmful agents.
Methods:
We developed a multimethod that combines the collection of a large amount of data from Twitter (~40,000 tweets), an automatic tweet classification algorithm, the K-means clustering algorithm, and a qualitative content analysis.
Results:
Our results show a larger number of topics (20 versus 17 clusters), greater coherence of topics (0.99 vs. 0.97) and distinctiveness of topics (1.22 vs. 1.31) among anti-vaccinists in comparison to pro-vaccinists. In addition, while anti-vaccinists use all four message frames known to make narratives persuasive and influential, pro-vaccinists neglect the problem statement.
Conclusions:
Based on our results, we attribute the diversity, coherence, and distinctiveness of topics discussed among anti-vaccinists to their higher engagement, and we ascribe the influence of vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing value propositions for vaccines to counteract the negative impact of anti-vaccine content on the uptake rates. Clinical Trial: This study was determined to be a non-human subject study by Michigan State University’s Institutional Review Board (#STUDY00004514).
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