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Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages and Conversational Hate Speech

Published:26 January 2022Publication History

ABSTRACT

The HASOC track is dedicated to the evaluation of technology for finding Offensive Language and Hate Speech. HASOC is creating a multilingual data corpus mainly for English and under-resourced languages(Hindi and Marathi). This paper presents one HASOC subtrack with two tasks. In 2021, we organized the classification task for English, Hindi, and Marathi. The first task consists of two classification tasks; Subtask 1A consists of a binary and fine-grained classification into offensive and non-offensive tweets. Subtask 1B asks to classify the tweets into Hate, Profane and offensive. Task 2 consists of identifying tweets given additional context in the form of the preceding conversion. During the shared task, 65 teams have submitted 652 runs. This overview paper briefly presents the task descriptions, the data and the results obtained from the participant’s submission.

References

  1. Kadam Aditya, Goel Anmol, Jain Jivitesh, Kalra Jushaan, Singh, Subramanian Mallika, Reddy Manvith, Kodali Prashant, H Arjun, T, Shrivastava Manish, and Kumaraguru Ponnurangam. 2021. Battling Hateful Content in Indic Languages HASOC ’21. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  2. Glazkova Anna, Kadantsev Michael, and Glazkov Maksim. 2021. Fine-tuning of Pre-trained Transformers for Hate, Offensive, and Profane Content Detection in English and Marathi. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  3. Mitra Arka and Sankhala Priyanshu. 2021. Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  4. Thomas Mandl, Sandip Modha, Anand Kumar M, and Bharathi Raja Chakravarthi. 2020. Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German. In FIRE 2020: Forum for Information Retrieval Evaluation, Hyderabad, India, December 16-20, 2020, Prasenjit Majumder, Mandar Mitra, Surupendu Gangopadhyay, and Parth Mehta (Eds.). ACM, 29–32. https://doi.org/10.1145/3441501.3441517Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Thomas Mandl, Sandip Modha, Prasenjit Majumder, Daksh Patel, Mohana Dave, Chintak Mandlia, and Aditya Patel. 2019. Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in indo-european languages. In Proceedings of the 11th forum for information retrieval evaluation. 14–17.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Thomas Mandl, Sandip Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schäfer, Tharindu Ranasinghe, Marcos Zampieri, Durgesh Nandini, and Amit Kumar Jaiswal. 2021. Overview of the HASOC subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages. In Working Notes of FIRE 2021 - Forum for Information Retrieval Evaluation. CEUR. http://ceur-ws.org/Google ScholarGoogle Scholar
  7. Nene Mayuresh, North Kai, Ranasinghe Tharindu, and Zampieri Marcos. 2021. Transformer Models for Offensive Language Identification in Marathi. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  8. Bhatia Mehar, Bhotia Tenzin, Singhay, Agarwal Akshat, Ramesh Prakash, Gupta Shubham, Shridhar Kumar, Laumann Felix, and Dash Ayushman. 2021. One to Rule Them All: Towards Joint Indic Language Hate Speech Detection. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  9. Sandip Modha, Prasenjit Majumder, Thomas Mandl, and Rishab Singla. 2021. Design and analysis of microblog-based summarization system. Social Network Analysis and Mining 11, 1 (2021), 1–16. https://doi.org/10.1007/s13278-021-00830-3Google ScholarGoogle ScholarCross RefCross Ref
  10. Bölücü Necva and Canbay Pelin. 2021. Hate Speech and Offensive Content Identification with Graph Convolutional Networks. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  11. Shrey Satapara, Sandip Modha, Thomas Mandl, Hiren Madhu, and Prasenjit Majumder. 2021. Overview of the HASOC Subtrack at FIRE 2021: Conversational Hate Speech Detection in Code-mixed language. In Working Notes of FIRE 2021 - Forum for Information Retrieval Evaluation. CEUR.Google ScholarGoogle Scholar
  12. Mundra Shikha, Singh Nikhil, and Mittal Namita. 2021. Fine-tune BERT to Classify Hate Speech in Hindi English Code-Mixed Text. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  13. Banerjee Somnath, Sarkar Maulindu, Agrawal Nancy, Saha Punyajoy, and Das Mithun. 2021. Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  14. Agustian Surya, Saputra Reski, and Fadhilah Aidil. 2021. “Feature Selection” with Pretrained-BERT for Hate Speech and Offensive Content Identification in English and Hindi Languages. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  15. Kui Yongyi. 2021. Detect Hate and Offensive Content in English and Indo-Aryan Languages based on Transformer. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  16. Bestgen Yves. 2021. A simple language-agnostic yet strong baseline system for hate speech and offensive content identification. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar
  17. Farooqi Zaki, Mustafa, Ghosh Sreyan, and Shah Rajiv, Ratn. 2021. Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets. In Forum for Information Retrieval Evaluation (Working Notes) (FIRE). CEUR-WS.org.Google ScholarGoogle Scholar

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  1. Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages and Conversational Hate Speech
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          • Published in

            cover image ACM Other conferences
            FIRE '21: Proceedings of the 13th Annual Meeting of the Forum for Information Retrieval Evaluation
            December 2021
            113 pages
            ISBN:9781450395960
            DOI:10.1145/3503162

            Copyright © 2021 Owner/Author

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 26 January 2022

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            Overall Acceptance Rate19of64submissions,30%

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