  Online abusive language detection (ALD) has become a societal issue of
increasing importance in recent years. Several previous works in online ALD
focused on solving a single abusive language problem in a single domain, like
Twitter, and have not been successfully transferable to the general ALD task or
domain. In this paper, we introduce a new generic ALD framework, MACAS, which
is capable of addressing several types of ALD tasks across different domains.
Our generic framework covers multi-aspect abusive language embeddings that
represent the target and content aspects of abusive language and applies a
textual graph embedding that analyses the user's linguistic behaviour. Then, we
propose and use the cross-attention gate flow mechanism to embrace multiple
aspects of abusive language. Quantitative and qualitative evaluation results
show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD
algorithms across seven ALD datasets covering multiple aspects of abusive
language and different online community domains.
