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We introduce a generic, language-independent method to collect a largepercentage of offensive and hate tweets regardless of their topics or genres.We harness the extralinguistic information embedded in the emojis to collect alarge number of offensive tweets. We apply the proposed method on Arabic tweetsand compare it with English tweets - analysing key cultural differences. Weobserved a constant usage of these emojis to represent offensiveness throughoutdifferent timespans on Twitter. We manually annotate and publicly release thelargest Arabic dataset for offensive, fine-grained hate speech, vulgar andviolence content. Furthermore, we benchmark the dataset for detectingoffensiveness and hate speech using different transformer architectures andperform in-depth linguistic analysis. We evaluate our models on externaldatasets - a Twitter dataset collected using a completely different method, anda multi-platform dataset containing comments from Twitter, YouTube andFacebook, for assessing generalization capability. Competitive results on thesedatasets suggest that the data collected using our method captures universalcharacteristics of offensive language. Our findings also highlight the commonwords used in offensive communications, common targets for hate speech,specific patterns in violence tweets; and pinpoint common classification errorsthat can be attributed to limitations of NLP models. We observe that evenstate-of-the-art transformer models may fail to take into account culture,background and context or understand nuances present in real-world data such assarcasm.

Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech