Name: Arabic Hate Speech 2022 Shared Task
Description: largest Arabic dataset for offensive, fine-grained hate speech, vulgar and violence content
Provider: QCRI
Paper Title: Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech
Tasks: offensive language detection, hate speech detection
Venue Name: nan
Authors: nan
Abstract: We introduce a generic, language-independent method to collect a large percentage of offensive and hate
  tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the
  emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets
  and compare it with English tweets – analysing key cultural differences. We observed a constant usage of
  these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate
  and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using
  different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on
  external datasets – a Twitter dataset collected using a completely different method, and a multi-platform
  dataset containing comments from Twitter, YouTube and Facebook, for assessing generalization capability.
  Competitive results on these datasets suggest that the data collected using our method captures universal
  characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets; and pinpoint common
  classification errors that can be attributed to limitations of NLP models. We observe that even state-ofthe-art transformer models may fail to take into account culture, background and context or understand
  nuances present in real-world data such as sarcasm.
  