Hate Speech Detection in Social Media Surveillance: A Review of Related Literature

  • Usman Ahmed Foundation University Islmabad https://orcid.org/0000-0002-9319-7345
  • Rahmet Bibi Superior University Lahore
  • Obaid Ullah Superior University Lahore
  • Rahat Bano Superior University Lahore
Keywords: data mining,, hate speech, NLP, semantics, sementic analysis, social media, surveillance, text mining


Abstract Views: 264

Social media surveillance is a   requirement   for   governments   and intelligence agencies around the world to detect and prevent hate crimes.  The dynamic and unstructured nature of the textual   content available on social media platforms makes it very complex to extract hate related speech patterns from   this   content.   It   also   creates ambiguities in the data and therefore, data mining techniques become difficult to apply in   this   scenario.   Several alternative techniques were adopted by different researchers in the past to cope with this  problem  and  to  capture  and analyze  such  unstructured  text  for  the purpose of hate speech detection. In this paper,  we  reviewed,  categorized  and presented   a   state-of-the-art   of   these techniques which were   divided   in to three  categories  namely  text  mining, sentiment  analysis  and  semantics.  The challenges   in the application   of   the existing techniques were also discussed and these can be taken up as future directions


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How to Cite
Ahmed, U., Bibi, R., Ullah, O., & Bano, R. (2021). Hate Speech Detection in Social Media Surveillance: A Review of Related Literature. Innovative Computing Review, 1(1), 01–11. https://doi.org/10.32350/icr.0101.01