Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter

Bibliographic Data

ID11029680
AuthorsAmelia M Jamison (0000-0002-3954-9581, Center for Health and Gender Equity), Amelia Jamison (Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...), David Broniatowski (0000-0002-3302-9497, Center for Health and Gender Equity), David A Broniatowski (Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...), Michael C Smith (0000-0002-1200-0855, Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...), Kajal S Parikh (Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...), Adeena Malik (Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...), Mark Dredze (0000-0002-0422-2474, Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...), Sandra C Quinn (0000-0002-3258-4595, Amelia M. Jamison, Kajal S. Parikh, and Adeena Malik are with the Maryland Center for Health Equity, School of Public Health, University of Maryland, College Park. David A. Broniatowski and Michael C. Smith are with the Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC. Mark Dredze is with the Department of Computer Science, Whiting School of Engineering...)
Year2020
Volume110
IssueS3
PagesS331-S339
Publication date2020-10-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Public Health (JOURNAL)
Journal identifiersISSN: 0090-0036 • E-ISSN: 1541-0048
PublisherAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.2020.305940
PMID33001737
OpenAlexW3089905796
LanguageEN
Citations received20
References cited27

Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter. Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set. Results. Manual annotation identified 22% of the data set as antivaccine, of which safety concerns and conspiracies were the most common themes. Seventeen percent of content was identified as provaccine, with roughly equal proportions of vaccine promotion, criticizing antivaccine beliefs, and vaccine safety and effectiveness. Of the 100 LDA topics, 48 contained provaccine sentiment and 28 contained antivaccine sentiment, with 9 containing both. Conclusions. Our updated typology successfully combines manual annotation with machine-learning methods to estimate the distribution of vaccine arguments, with greater detail on the most distinctive topics of discussion. With this information, communication efforts can be developed to better promote vaccines and avoid amplifying antivaccine rhetoric on Twitter

Annotation · Information retrieval · Latent Dirichlet allocation · Misinformation · Political science · Promotion (chess) · Set (abstract data type) · Sociology · Topic model · Typology · Artificial Intelligence · Computer Science · Hate Speech and Cyberbullying Detection · Misinformation and Its Impacts · Psychology · Vaccine Coverage and Hesitancy

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