It's time to take a new approach to addressing negative messaging about vaccines, including avoiding the use of the term "anti-vaxxers", say the researchers.
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There was a marked increase in negativity about vaccines on Twitter after COVID-19 vaccines became available, the ESCMID Global Congress (formerly ECCMID) in Barcelona, Spain (27-30 April) will hear.
The analysis also found that spikes in the number of negative tweets coincided with announcements from governments and healthcare authorities about vaccination.
It's time to take a new approach to addressing negative messaging about vaccines, including avoiding the use of the term "anti-vaxxers", say the researchers.
"Vaccines are one of humanity's greatest achievements," explains lead researcher Dr Guillermo Rodriguez-Nava, of Stanford University School of Medicine, Stanford, USA.
"They have the potential to eradicate dangerous diseases such as smallpox, prevent deaths from diseases with 100% mortality rates, like rabies, and prevent cancers such as those caused by HPV.
"Moreover, vaccines can prevent complications from diseases for which we have limited treatment options, such as influenza and COVID-19, but there has been growing opposition to their use in recent years.
"The damage caused by negative voices is already apparent, with clusters of measles re-emerging in countries where it was previously considered eradicated.
"This situation harms children who cannot make decisions for themselves regarding vaccines, as well as immunocompromised patients who are unable to get vaccinated."
Dr Rodriguez-Nava and colleagues analysed the impact of the introduction of COVID-19 vaccines on the sentiment of vaccine-related posts on Twitter.
Open-source software (the Snscrape library in Python) was used to download tweets with the hashtag "vaccine" from January 1 2018 to December 31 2022.
Cutting-edge AI methods were then used to perform sentiment analysis and classify as the tweets having either positive or negative sentiment. Finally, modelling techniques were used to create a "counterfactual scenario". This showed what the pattern of tweets would have looked like if COVID vaccines hadn't been introduced in December 2020.
567,915 tweets were extracted and analysed. 458,045 classified were negative and 109,870 as positive by the machine learning algorithm. Tweets that were negative in sentiment were predominant both before and after vaccines became available
Negative tweets included: "The EU Commission should immediately terminate contracts for new doses of fake #vaccines against #COVID19 and demand the return of the 2.5 billion euros paid so far. Everyone who lied that #vaccines prevent the spread of the virus must be held accountable."
