  As of today, abuse is a pressing issue to participants and administrators of
Online Social Networks (OSN). Abuse in Twitter can spawn from arguments
generated for influencing outcomes of a political election, the use of bots to
automatically spread misinformation, and generally speaking, activities that
deny, disrupt, degrade or deceive other participants and, or the network. Given
the difficulty in finding and accessing a large enough sample of abuse ground
truth from the Twitter platform, we built and deployed a custom crawler that we
use to judiciously collect a new dataset from the Twitter platform with the aim
of characterizing the nature of abusive users, a.k.a abusive birds, in the
wild. We provide a comprehensive set of features based on users' attributes, as
well as social-graph metadata. The former includes metadata about the account
itself, while the latter is computed from the social graph among the sender and
the receiver of each message. Attribute-based features are useful to
characterize user's accounts in OSN, while graph-based features can reveal the
dynamics of information dissemination across the network. In particular, we
derive the Jaccard index as a key feature to reveal the benign or malicious
nature of directed messages in Twitter. To the best of our knowledge, we are
the first to propose such a similarity metric to characterize abuse in Twitter.
