Abstract
Aggression is a prominent trait of human beings that can affect social harmony in a negative way. The hate mongers misuse the freedom of speech in social media platforms to flood with their venomous comments in many forms. Identifying different traits of online offense is thus inevitable and the need of the hour. Existing studies usually handle one or two offense traits at a time, mainly due to the lack of a combined annotated dataset and a scientific study that provides insights into the relationship among the traits. In this paper, we study the relationship among five offense traits – aggression, hate, sarcasm, humor, and stance in Hinglish (Hindi-English) social media code-mixed texts. We employ various state-of-the-art deep learning systems at different morphological granularities for the classification across five offense traits. Our evaluation of the unified framework suggests ∼90% performance across all major traits. Furthermore, we propose a novel notion of causal importance score to quantify the effect of different abusive keywords and the overall context on the offensiveness of the texts.
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