  Misinformation on social media presents a major threat to modern societies.
While previous research has analyzed the virality across true and false social
media posts, not every misleading post is necessarily equally viral. Rather,
misinformation has different characteristics and varies in terms of its
believability and harmfulness - which might influence its spread. In this work,
we study how the perceived believability and harmfulness of misleading posts
are associated with their virality on social media. Specifically, we analyze
(and validate) a large sample of crowd-annotated social media posts from
Twitter's Birdwatch platform, on which users can rate the believability and
harmfulness of misleading tweets. To address our research questions, we
implement an explanatory regression model and link the crowd ratings for
believability and harmfulness to the virality of misleading posts on Twitter.
Our findings imply that misinformation that is (i) easily believable and (ii)
not particularly harmful is associated with more viral resharing cascades.
These results offer insights into how different kinds of crowd fact-checked
misinformation spreads and suggest that the most viral misleading posts are
often not the ones that are particularly concerning from the perspective of
public safety. From a practical view, our findings may help platforms to
develop more effective strategies to curb the proliferation of misleading posts
on social media.
