  With rising concern around abusive and hateful behavior on social media
platforms, we present an ensemble learning method to identify and analyze the
linguistic properties of such content. Our stacked ensemble comprises of three
machine learning models that capture different aspects of language and provide
diverse and coherent insights about inappropriate language. The proposed
approach provides comparable results to the existing state-of-the-art on the
Twitter Abusive Behavior dataset (Founta et al. 2018) without using any user or
network-related information; solely relying on textual properties. We believe
that the presented insights and discussion of shortcomings of current
approaches will highlight potential directions for future research.
