  In Twitter, there is a rising trend in abusive behavior which often leads to
incivility. This trend is affecting users mentally and as a result they tend to
leave Twitter and other such social networking sites thus depleting the active
user base. In this paper, we study factors associated with incivility. We
observe that the act of incivility is highly correlated with the opinion
differences between the account holder (i.e., the user writing the incivil
tweet) and the target (i.e., the user for whom the incivil tweet is meant for
or targeted), toward a named entity. We introduce a character level CNN model
and incorporate the entity-specific sentiment information for efficient
incivility detection which significantly outperforms multiple baseline methods
achieving an impressive accuracy of 93.3% (4.9% improvement over the best
baseline). In a post-hoc analysis, we also study the behavioral aspects of the
targets and account holders and try to understand the reasons behind the
incivility incidents. Interestingly, we observe that there are strong signals
of repetitions in incivil behavior. In particular, we find that there are a
significant fraction of account holders who act as repeat offenders - attacking
the targets even more than 10 times. Similarly, there are also targets who get
targeted multiple times. In general, the targets are found to have higher
reputation scores than the account holders.
