Purpose Automated social media messaging tactics can undermine trust in health institutions and public health advice. As such, we examine automated software programs (ASPs) and social bots in the Twitter anti-vaccine discourse before and after the release of COVID-19 vaccines. Design/methodology/approach We compare two Twitter datasets comprising user accounts and associated English-language tweets featuring the keywords “#antivaxx” or “anti-vaxx.” The first dataset, from 2018 (pre-COVID vaccine), includes 3,154 user accounts and 6,380 tweets. The second comprises 327,067 accounts and 545,268 tweets published during the 12 months following December 1, 2020 (post-COVID vaccine). Using Information Laundering Theory (ILT), the datasets were examined manually and through user analytics and machine learning to identify activity, visibility, verification status, vaccine position, and ASP or bot technology use. Findings The post-COVID vaccine dataset showed an increase in highly probable bot accounts (31.09%) and anti-vaccine accounts. However, both datasets were dominated by pro-vaccine accounts; most highly active (59%) and highly visible (50%) accounts classified as probable bots were pro-vaccine. Originality/value This research is the first to compare bot behaviors in the “#antivaxx” discourse before and after the release of COVID-19 vaccines. The prevalence of mostly benevolent probable bot accounts suggests a potential overstatement of the threat posed by anti-vaccine accounts using ASPs or bot technologies. By highlighting bots as intermediaries that disseminate both pro- and anti-vaccine content, we extend ILT by identifying a benevolent variant and offering insights into bots as “pathways” to generating mainstream information.