To be a pro-vax or not, the COVID-19 vaccine conundrum on Twitter

TL;DR

The temporal study shows that the change-of-stance behaviour became really significant once the trial results of COVID-19 vaccine were announced to the public, and it appears as the change of stance towards pro-vax is a reaction to people changing their opinion towards anti-vaxy.

Abstract

The most surprising observation reported by the study in (arXiv:2208.13523), involving stance detection of COVID-19 vaccine related tweets during the first year of pandemic, is the presence of a significant number of users (~2 million) who posted tweets with both anti-vax and pro-vax stances. This is a sizable cohort even when the stance detection noise is considered. In this paper, we tried to get deeper understanding of this 'dual-stance' group. Out of this group, 60% of users have more pro-vax tweets than anti-vax tweets and 17% have the same number of tweets in both classes. The rest have more anti-vax tweets, and they were highly active in expressing concerns about mandate and safety of a fast-tracked vaccine, while also tweeted some updates about vaccine development. The leaning pro-vax group have opposite composition: more vaccine updates and some posts about concerns. It is important to note that vaccine concerns were not always genuine and had a large dose of misinformation. 43% of the balanced group have only tweeted one tweet of each type during our study period and are the less active participants in the vaccine discourse. Our temporal study also shows that the change-of-stance behaviour became really significant once the trial results of COVID-19 vaccine were announced to the public, and it appears as the change of stance towards pro-vax is a reaction to people changing their opinion towards anti-vax. Our study finished at Mar 23, 2021 when the conundrum was still going strong. The dilemma might be a reflection of the uncertain and stressful times, but it also highlights the importance of building public trust to combat prevalent misinformation.

Figures (10)

• Figure 1: Pro-leaning, anti-leaning, and balanced users with respective anti-vax and pro-vax tweets.
• Figure 2: Top 25 anti-vax and pro-vax discussion topics for dual-stance users' tweets. Lighter shade bars represent the expected number of tweets, and darker bars represent the actual observation. (a) For all dual-stance users, (b) for anti-leaning users with $p_i >= 0.9$, (c) for balanced users with $p_i >= 0.9$, (d) for pro-leaning users with $p_i >= 0.9$.
• Figure 3: Genuine issues versus falsehoods classification of anti-vax tweets.
• Figure 4: Total stance changes versus total anti-vax and pro-vax tweets for anti-leaning, balanced, and pro-leaning user groups. Insets are zoomed-in plots for respective groups.
• Figure 5: An example of a user's timeline who is oscillating between anti-vax and pro-vax stances. Actual tweets are summarised for better visualisation and to avoid user identification.