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        <title>Emojis Trash or Treasure: Utilizing Emoji to Aid Hate Speech Detection</title>
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    <name type="personal">
        <namePart type="given">Tanik</namePart>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>In this study, we delve into the fascinating realm of emojis and their impact on identifying hate speech in both Bengali and English languages. Through extensive exploration of various techniques, particularly the integration of Multilingual BERT (MBert) and Emoji2Vec embeddings, we strive to shed light on the immense potential of emojis in this detection process. By meticulously comparing these advanced models with conventional approaches, we uncover the intricate contextual cues that emojis bring to the table. Ultimately, our discoveries underscore the invaluable role of emojis in hate speech detection, thereby providing valuable insights for the creation of resilient and context-aware systems to combat online toxicity. Our findings showcase the potential of emojis as valuable assets rather than mere embellishments in the realm of hate speech detection. By leveraging the combined strength of MBert and Emoji2Vec, our models exhibit enhanced capabilities in deciphering the emotional subtleties often intertwined with hate speech expressions.</abstract>
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%0 Conference Proceedings
%T Emojis Trash or Treasure: Utilizing Emoji to Aid Hate Speech Detection
%A Saikh, Tanik
%A Barman, Soham
%A Kumar, Harsh
%A Sahu, Saswat
%A Palit, Souvick
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F saikh-etal-2024-emojis
%X In this study, we delve into the fascinating realm of emojis and their impact on identifying hate speech in both Bengali and English languages. Through extensive exploration of various techniques, particularly the integration of Multilingual BERT (MBert) and Emoji2Vec embeddings, we strive to shed light on the immense potential of emojis in this detection process. By meticulously comparing these advanced models with conventional approaches, we uncover the intricate contextual cues that emojis bring to the table. Ultimately, our discoveries underscore the invaluable role of emojis in hate speech detection, thereby providing valuable insights for the creation of resilient and context-aware systems to combat online toxicity. Our findings showcase the potential of emojis as valuable assets rather than mere embellishments in the realm of hate speech detection. By leveraging the combined strength of MBert and Emoji2Vec, our models exhibit enhanced capabilities in deciphering the emotional subtleties often intertwined with hate speech expressions.
%U https://aclanthology.org/2024.icon-1.64/
%P 554-561

Markdown (Informal)

[Emojis Trash or Treasure: Utilizing Emoji to Aid Hate Speech Detection](https://aclanthology.org/2024.icon-1.64/) (Saikh et al., ICON 2024)

• Emojis Trash or Treasure: Utilizing Emoji to Aid Hate Speech Detection (Saikh et al., ICON 2024)

ACL

• Tanik Saikh, Soham Barman, Harsh Kumar, Saswat Sahu, and Souvick Palit. 2024. Emojis Trash or Treasure: Utilizing Emoji to Aid Hate Speech Detection. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 554–561, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).