  Hate speech, offensive language, sexism, racism and other types of abusive
behavior have become a common phenomenon in many online social media platforms.
In recent years, such diverse abusive behaviors have been manifesting with
increased frequency and levels of intensity. This is due to the openness and
willingness of popular media platforms, such as Twitter and Facebook, to host
content of sensitive or controversial topics. However, these platforms have not
adequately addressed the problem of online abusive behavior, and their
responsiveness to the effective detection and blocking of such inappropriate
behavior remains limited.
  In the present paper, we study this complex problem by following a more
holistic approach, which considers the various aspects of abusive behavior. To
make the approach tangible, we focus on Twitter data and analyze user and
textual properties from different angles of abusive posting behavior. We
propose a deep learning architecture, which utilizes a wide variety of
available metadata, and combines it with automatically-extracted hidden
patterns within the text of the tweets, to detect multiple abusive behavioral
norms which are highly inter-related. We apply this unified architecture in a
seamless, transparent fashion to detect different types of abusive behavior
(hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for
any tuning of the model architecture for each task. We test the proposed
approach with multiple datasets addressing different and multiple abusive
behaviors on Twitter. Our results demonstrate that it largely outperforms the
state-of-art methods (between 21 and 45\% improvement in AUC, depending on the
dataset).
