Characterizing Sociolinguistic Variation in the Competing Vaccination Communities

TL;DR

This paper analyzes sociolinguistic differences between pro- and anti-vaccination communities on Twitter to inform targeted health communication strategies, revealing significant linguistic and network-level distinctions that can guide effective messaging.

Contribution

It introduces a sociolinguistic and network analysis of vaccination communities on Twitter, highlighting key linguistic and structural differences to improve health misinformation interventions.

Findings

Significant linguistic differences in intensifiers, pronouns, and uncertainty words.

Differences in network density, echo-chamberness, and EI index.

Sociolinguistic features can serve as proxies for community characterization.

Abstract

Public health practitioners and policy makers grapple with the challenge of devising effective message-based interventions for debunking public health misinformation in cyber communities. "Framing" and "personalization" of the message is one of the key features for devising a persuasive messaging strategy. For an effective health communication, it is imperative to focus on "preference-based framing" where the preferences of the target sub-community are taken into consideration. To achieve that, it is important to understand and hence characterize the target sub-communities in terms of their social interactions. In the context of health-related misinformation, vaccination remains to be the most prevalent topic of discord. Hence, in this paper, we conduct a sociolinguistic analysis of the two competing vaccination communities on Twitter: "pro-vaxxers" or individuals who believe in the effectiveness of vaccinations, and "anti-vaxxers" or individuals who are opposed to vaccinations. Our data analysis show significant linguistic variation between the two communities in terms of their usage of linguistic intensifiers, pronouns, and uncertainty words. Our network-level analysis show significant differences between the two communities in terms of their network density, echo-chamberness, and the EI index. We hypothesize that these sociolinguistic differences can be used as proxies to characterize and understand these communities to devise better message interventions.