COVID-19 Vaccine Fact-Checking Posts on Facebook: Observational Study

Abstract

Background Effective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy. Objective This work investigates (1) the changes in the public's attitude toward COVID-19 vaccines over time, (2) the effectiveness of COVID-19 vaccine fact-checking information on social media engagement and attitude change, and (3) the emotional and linguistic features of the COVID-19 vaccine fact-checking information ecosystem. Methods We collected a data set of 12,553 COVID-19 vaccine fact-checking Facebook posts and their associated comments (N=122,362) from January 2020 to March 2022 and conducted a series of natural language processing and statistical analyses to investigate trends in public attitude toward the vaccine in COVID-19 vaccine fact-checking posts and comments, and emotional and linguistic features of the COVID-19 fact-checking information ecosystem. Results The percentage of fact-checking posts relative to all COVID-19 vaccine posts peaked in May 2020 and then steadily decreased as the pandemic progressed (r=–0.92, df=21, t=–10.94, 95% CI –0.97 to –0.82, P<.001). The salience of COVID-19 vaccine entities was significantly lower in comments (mean 0.03, SD 0.03, t=39.28, P<.001) than in posts (mean 0.09, SD 0.11). Third-party fact checkers have been playing a more important role in more fact-checking over time (r=0.63, df=25, t=4.06, 95% CI 0.33-0.82, P<.001). COVID-19 vaccine fact-checking posts continued to be more analytical (r=0.81, df=25, t=6.88, 95% CI 0.62-0.91, P<.001) and more confident (r=0.59, df=25, t=3.68, 95% CI 0.27-0.79, P=.001) over time. Although comments did not exhibit a significant increase in confidence over time, tentativeness in comments significantly decreased (r=–0.62, df=25, t=–3.94, 95% CI –0.81 to –0.31, P=.001). In addition, although hospitals receive less engagement than other information sources, the comments expressed more positive attitudinal valence in comments compared to other information sources (b=0.06, 95% CI 0.00-0.12, t=2.03, P=.04). Conclusions The percentage of fact-checking posts relative to all posts about the vaccine steadily decreased after May 2020. As the pandemic progressed, third-party fact checkers played a larger role in posting fact-checking COVID-19 vaccine posts. COVID-19 vaccine fact-checking posts continued to be more analytical and more confident over time, reflecting increased confidence in posts. Similarly, tentativeness in comments decreased; this likewise suggests that public uncertainty diminished over time. COVID-19 fact-checking vaccine posts from hospitals yielded more positive attitudes toward vaccination than other information sources. At the same time, hospitals received less engagement than other information sources. This suggests that hospitals should invest more in generating engaging public health campaigns on social media.

Mitigating Vaccine Hesitancy and Building Trust to Prevent Future Measles Outbreaks in England

“…Global advocacy is needed for standardising online fact-checking for immunisation posts on social media. Fact-checking posts have become increasingly common, particularly throughout the COVID-19 pandemic, and have demonstrated their necessity in increasing vaccine confidence and positive attitudes [ 122 ].…”

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

“…Socioeconomically disadvantaged groups were more likely to hold polarized opinions on COVID-19 vaccines [ 22 ]. People with bad experiences during the pandemic were more likely to hold antivaccine opinions [ 22 ], whereas comments on posts from health media and hospitals had more positive attitudes [ 74 ]. Social media posts on Pfizer and Moderna vaccines appeared to be more positive than posts on COVID-19 vaccines from other manufacturers [ 24 , 77 , 83 ].…”

“…Nineteen sentiment analysis studies were based on machine learning, and they trained machine learning classifiers by annotating tweets in the data set [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77]. Machine learning approaches used supervised classification algorithms to extract information regarding sentiment polarity.…”

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

“…We can even tell that the availability of such models leads to the democratization of this NLP approach. Indeed, without the help of NLP researchers, biologists, radiologists, pharmacists and other clinicians can now use these models within the clinical context and we can find several such experiments, such as: analysis of tweets for user opinions and side effects on COVID-19 vaccines [66,33], fact-checking of posts on COVID-19 vaccines [67], identification of social determinants of health in EHRs [68], analysis of literature for drug-induced liver injury [69], labeling of diagnosis in cardiovascular Magnetic resonance imaging (MRI) [70], analysis of social media on the quality of life in Parkinson's patients [31], extraction of biomedical relations from the scientific literature [71].…”