  The increased use of online social networks for the dissemination of
information comes with the misuse of the internet for cyberbullying,
cybercrime, spam, vandalism, amongst other things. To proactively identify
abuse in the networks, we propose a model to identify abusive posts by
crowdsourcing. The crowdsourcing part of the detection mechanism is implemented
implicitly, by simply observing the natural interaction between users
encountering the messages. We explore the node-to-node spread of information on
Twitter and propose a model that predicts the abuse level (abusive, hate, spam,
normal) associated with the tweet by observing the attributes of the message,
along with those of the users interacting with it. We demonstrate that the
difference in users' interactions with abusive posts can be leveraged in
identifying posts of varying abuse levels.
