Vaccine misinformation types and properties in Russian troll tweets

Abstract

Objective: To identify the content of and engagement with vaccine misinformation from Russian trolls on Twitter.

Methods: Troll tweets (N = 1959) obtained from Twitter in 2020 were coded for vaccine misinformation (α = 0.77-0.97). Descriptive, bivariate, and multivariable negative binomial regressions were applied to estimate robust incidence rate ratios (IRRs) and 95% confidence intervals (95 %CI) of vaccine misinformation associations with tweet characteristics and engagement (i.e., replies, likes, retweets).

Results: Misinformation about personal dangers (43.0%), civil liberty violations (20.2%), and vaccine conspiracies (18.6%) were common. More misinformation tweets used anti-vaccination language (97.3% vs. 13.2%) and referenced symptoms (37.4% vs. 0.5%) than non-misinformation tweets. Fewer misinformation tweets referenced credible sources (14.0% vs. 19.5%), were formatted as headlines (39.2% vs. 77.0%), and mentioned specific vaccines (11.3% vs. 36.1%, all p < 0.01) than non-misinformation tweets. Personal dangers misinformation had 83% lower rate of retweets (95 %CI 0.04-0.66). Civil liberties misinformation had significantly higher rate of replies (IRR: 7.65, 95 %CI 1.06-55.46), but lower overall engagement (IRR: 0.38, 95 %CI 0.16-0.88) than non-misinformation tweets.

Conclusions: Strategies used to promote vaccine misinformation provide insight into the nature of vaccine misinformation online and public responses. Our findings suggest a need to explore influences on whether users reject or entertain online vaccine misinformation.

Keywords: Communication; Disinformation; Misinformation; Social media; Tweet; Twitter.