  The original goal of any social media platform is to facilitate users to
indulge in healthy and meaningful conversations. But more often than not, it
has been found that it becomes an avenue for wanton attacks. We want to
alleviate this issue and hence we try to provide a detailed analysis of how
abusive behavior can be monitored in Twitter. The complexity of the natural
language constructs makes this task challenging. We show how applying
contextual attention to Long Short Term Memory networks help us give near state
of art results on multiple benchmarks abuse detection data sets from Twitter.
