  Coordinated campaigns are used to influence and manipulate social media
platforms and their users, a critical challenge to the free exchange of
information online. Here we introduce a general, unsupervised network-based
methodology to uncover groups of accounts that are likely coordinated. The
proposed method constructs coordination networks based on arbitrary behavioral
traces shared among accounts. We present five case studies of influence
campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong
protests, the Syrian civil war, and cryptocurrency manipulation. In each of
these cases, we detect networks of coordinated Twitter accounts by examining
their identities, images, hashtag sequences, retweets, or temporal patterns.
The proposed approach proves to be broadly applicable to uncover different
kinds of coordination across information warfare scenarios.
