Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter

Abstract

Many states in the United States allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine can have direct consequences in public health. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ efforts of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining momentum. Studies have shown that bots on social media (i.e., social bots) can influence opinion trends by posting a substantial number of automated messages. The research presented here investigates the communication patterns of anti- and pro-vaccine users and the role of bots in Twitter by studying a retweet network related to MMR vaccine after the 2015 California Disneyland measles outbreak. We first classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups using supervised machine learning. We discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group. In addition, our bot analysis discovers that 1.45% of the corpus users were identified as likely bots which produced 4.59% of all tweets within our dataset. We further found that bots display hyper-social tendencies by initiating retweets at higher frequencies with users within the same opinion group. The article concludes that highly clustered anti-vaccine Twitter users make it difficult for health organizations to penetrate and counter opinionated information while social bots may be deepening this trend. We believe that these findings can be useful in developing strategies for health communication of vaccination.

Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases

“…19,34 Content appears to transfer between users who share similar sentiments regarding vaccination but rarely across those with differing opinions, suggesting the structure of such platforms may give the illusion of debate, but in practice mainly serves to reinforce previously-held opinions rather than the consideration of new ones. 40 Such ideologic isolation may limit public health penetration to promote vaccination on social media. 40 More recently, the rapid spread of COVID-19 and resultant global pandemic have become a focus of intense social media discourse, with Twitter reporting a COVID-19 related tweet every 45 milliseconds and the hashtag #coronavirus rapidly becoming the 2 nd most used in 2020.…”

“…40 Such ideologic isolation may limit public health penetration to promote vaccination on social media. 40 More recently, the rapid spread of COVID-19 and resultant global pandemic have become a focus of intense social media discourse, with Twitter reporting a COVID-19 related tweet every 45 milliseconds and the hashtag #coronavirus rapidly becoming the 2 nd most used in 2020. 41,42 Unfortunately, false and misleading information about COVID-19, potentially dangerous treatments, and eventual vaccination continues to grow on social media platforms.…”

Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

“…Although so-called "anti-vaxxers" are a minority of the population, they are a vocal and growing community on social media platforms like Twitter [28]. The communities that form around these sentiments also tend to be "highly clustered" [66], engaging with one another, but not with other networks of users [28]. They are also vulnerable to misinformation, which spreads easily on social media [6], and is most rampant among the overconfident -that is, those who think they know more than experts [39].…”

#Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses

“…A network analysis method has been used to observe antivaccination movements on social media. Yuan et al [ 61 ] analyzed the retweet network related to the MMR vaccine; they discovered that users with pro- and antivaccine views retweet much more from their own opinion communities. Himelboim et al [ 62 ] also found subgroups regarding the HPV vaccine conversation on Twitter, noting that interactions and information flows mainly occurred inside each subgroup.…”