  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.
