  With the emergence and rapid proliferation of social media platforms and
social networking sites, recent years have witnessed a surge of misinformation
spreading in our daily life. Drawing on a large-scale dataset which covers more
than 1.4M posts and 18M comments, we investigate the propagation of two
distinct narratives--(i) conspiracy information, whose claims are generally
unsubstantiated and thus referred as misinformation to some extent, and (ii)
scientific information, whose origins are generally readily identifiable and
verifiable--in an online social media platform. We find that conspiracy
cascades tend to propagate in a multigenerational branching process while
science cascades are more likely to grow in a breadth-first manner.
Specifically, conspiracy information triggers larger cascades, involves more
users and generations, persists longer, is more viral and bursty than science
information. Content analysis reveals that conspiracy cascades contain more
negative words and emotional words which convey anger, fear, disgust, surprise
and trust. We also find that conspiracy cascades are more concerned with
political and controversial topics. After applying machine learning models, we
achieve an AUC score of nearly 90% in discriminating conspiracy from science
narratives.
  We find that conspiracy cascades are more likely to be controlled by a
broader set of users than science cascades, imposing new challenges on the
management of misinformation. Although political affinity is thought to affect
the consumption of misinformation, there is very little evidence that political
orientation of the information source plays a role during the propagation of
conspiracy information. Our study provides complementing evidence to current
misinformation research and has practical policy implications to stem the
propagation and mitigate the influence of misinformation online.
