  Social media platforms such as Twitter have a fundamental role in
facilitating the spread and discussion of ideas online through the concept of
retweeting and replying. However, these features also contribute to the spread
of mis/disinformation during the vaccine rollout of the COVID-19 pandemic.
Using COVID-19 vaccines as a case study, we analyse multiple social network
representation derived from three message-based interactions on Twitter (quote
retweets, mentions and replies) based upon a set of known anti-vax hashtags and
keywords. Each network represents a certain hashtag or keyword which were
labelled as "controversial" and "non-controversial" according to a small group
of participants. For each network, we extract a combination of global and local
network-based metrics which are used as feature vectors for binary
classification. Our results suggest that it is possible to detect controversial
from non-controversial terms with high accuracy using simple network-based
metrics. Furthermore, these results demonstrate the potential of network
representations as language-agnostic models for detecting mis/disinformation at
scale, irrespective of content and across multiple social media platforms.
