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

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arxiv 2112.09301 v1 pith:6TGHMEAA submitted 2021-12-17 cs.CL cs.AIcs.SI

Thomas Mandl , Sandip Modha , Gautam Kishore Shahi , Hiren Madhu , Shrey Satapara , Prasenjit Majumder , Johannes Schaefer , Tharindu Ranasinghe
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Marcos Zampieri Durgesh Nandini Amit Kumar Jaiswal
This is my paper · ORCID
classification cs.CLcs.AIcs.SI
keywords hateoffensivedataenglishspeechclassificationcontenthasoc
verification ladder T0 review T1 audit T2 compute T3 formal
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The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.

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Cited by 3 Pith papers

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  1. A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance

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    A taxonomy of 14 hate speech definition components plus evidence that zero-shot LLM hate speech classification is sensitive to which definition is inserted into the prompt, with model-dependent effects.

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    A gated fusion head that conditions English toxicity, Indic abuse, and rule-based severity scores on the text context improves code-mixed abuse detection in 10/12 in-domain and 7/8 transfer comparisons.

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    A MURIL-based classifier outperforms RoBERTa, IndicBERT, and several published baselines on six Hinglish cyberbullying datasets, with reported gains of 1.36 to 13.07 percentage points.

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