Open AccessAdapting and Extending a Typology to Identify Vaccine Misinformation on Twitter
Author(s) -
Amelia Jamison,
David A. Broniatowski,
Michael C. Smith,
Kajal S. Parikh,
Adeena Malik,
Mark Dredze,
Sandra Crouse Quinn
Publication year - 2020
Publication title -
american journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.284
H-Index - 264
eISSN - 1541-0048
pISSN - 0090-0036
DOI - 10.2105/ajph.2020.305940
Subject(s) - misinformation , typology , latent dirichlet allocation , annotation , topic model , set (abstract data type) , computer science , promotion (chess) , information retrieval , psychology , artificial intelligence , sociology , political science , computer security , politics , anthropology , law , programming language
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.