This article presents data that are further analyzed and interpreted in “Shouting at Each Other into the Void: A Semantic Network Analysis of Vaccine Hesitance and Support in Online Discourse Regarding California Law SB277” . This research modified snowball sampling, a technique usually used to generate chains of informants that illuminate the structure of social networks, to collect digital documents following a chain of web links and recommendations, thus illuminating the underlying social, technical, and linguistic structure of online discourse. The resulting documents were manually coded according to the attitude towards vaccines they represented and/or the position they took with regard to California Senate Bill 277, a vaccine mandate policy that banned all nonmedical exemptions from school immunization requirements. Each attitude category, as well as the dataset as a whole, was subjected to quantitative linguistic analysis to identify key words and phrases in the data according to the frequency with which they appeared. A combination of that technique and semantic network analysis were used to generate clusters of related words that could be used for qualitative and narrative analysis, as detailed in the companion paper. The data collection and analysis processes described here will be of use to researchers conducting mixed-method analysis of online discourse who want their data to reflect the potential information and digital resources available to individuals who attempt to inform themselves about a particular topic using Internet searches. The data presented here could be useful for anyone seeking deeper insight into the linguistic and narrative patterns surrounding online debates about vaccination, controversial government policies, or both.