  Rampant use of offensive language on social media led to recent efforts on
automatic identification of such language. Though offensive language has
general characteristics, attacks on specific entities may exhibit distinct
phenomena such as malicious alterations in the spelling of names. In this
paper, we present a method for identifying entity specific offensive language.
We employ two key insights, namely that replies on Twitter often imply
opposition and some accounts are persistent in their offensiveness towards
specific targets. Using our methodology, we are able to collect thousands of
targeted offensive tweets. We show the efficacy of the approach on Arabic
tweets with 13% and 79% relative F1-measure improvement in entity specific
offensive language detection when using deep-learning based and support vector
machine based classifiers respectively. Further, expanding the training set
with automatically identified offensive tweets directed at multiple entities
can improve F1-measure by 48%.
