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Aggressive and Offensive Language Identification in Hindi, Bangla, and English: A Comparative Study

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Abstract

In the present paper, we carry out a comparative study between offensive and aggressive language and attempt to understand their inter-relationship. To carry out this study, we develop classifiers for offensive and aggressive language identification in Hindi, Bangla, and English using the datasets released for the languages as part of the two shared tasks: hate speech and offensive content identification in Indo-European languages (HASOC) and aggression and misogyny identification task at TRAC-2. The HASOC dataset is annotated with the information about offensive language and TRAC-2 dataset is annotated with the information about aggressive language. We experiment with SVM as well as BERT and its different derivatives such as ALBERT and DistilBERT for developing the classifiers. The best classifiers achieve an impressive F-score in between 0.70 and 0.80 for different tasks. We use these classifiers to cross-annotate the two datasets, and look at the co-occurrence of different sub-categories of aggression and offense. The study shows that even though aggression and offense significantly overlaps, but still one does not entail the other.

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Notes

  1. Please note that in both these tasks train, dev/validation and test sets were provided separately. In this table, the figures for train refer to the total of train and dev datasets—in our experiments also, we use the two datasets together for training the system and use the test set for reporting the system performance.

  2. In this section, we discuss the experiments related to the development of classifiers for sub-task A and sub-task B of HASOC shared task and sub-task A of the TRAC-2 shared task. For experiments related to sub-task C of HASOC and sub-task B of TRAC-2, you may refer to [33] and [5, 27].

  3. https://github.com/kaushaltrivedi/fast-bert.

  4. https://github.com/huggingface/pytorch-transformers.

  5. https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384.

  6. https://medium.com/huggingface/multi-label-text-classification-using-bert-the-mighty-transformer-69714fa3fb3d.

  7. The precision, recall and F-score reported in this paper are calculated using the scitkit-learn classification_report function, which gives an averaged F-score weighted by the total ‘support’ for each class—this, as expected, has resulted in some F-scores that do not lie in between the precision and recall values.

  8. I could not find any previous study that directly compares or studies the inter-relation between these phenomena and the assumption about their independence or being synonymous is largely implicit in the silence of the most of the researchers working in these areas. However, I have discussed some notable exceptions in “ Introduction”.

  9. Utterances are generally considered to be present only in spoken speech. However, considering social media comments to be a close approximation of speech, it would be more logical to divide these comments into utterances than sentences. In speech, utterances are characterised by short pauses while speaking. We considered the presence of single or multiple sentence-terminating punctuation, viz., full stop, exclamation mark, and question mark, as indicative of one utterance. Thus, these are used for calculating the numbers about punctuation.

  10. The 300-dimensional vectors of each of the lexical items in the GLoVe model were reduced to 2-dimension using principal component analysis (PCA) and then plotted as a scatter diagram to generate this visualisation—it is a standard and well-accepted method for visualising high-dimensional vectors in lower dimensions while studying similarity.

  11. We would like to reiterate here that simply use of profane word do not make the comment offensive—profanity is one of the factors that may lead to offense. However, as has been shown in this study neither of the two entail the other.

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Acknowledgements

We would like to thank the organisers of HASOC and TRAC-2 shared tasks for making the datasets publicly available, which has enabled us to carry out this research.

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Authors and Affiliations

  1. Department of Linguistics, K. M. Institute of Hindi and Linguistics, Dr. Bhimrao Ambedkar University, Agra, India

    Ritesh Kumar

  2. Department of Humanities and Social Sciences, Indian Institute of Technology, Kharagpur, India

    Bornini Lahiri

  3. Panlingua Language Processing LLP, New Delhi, India

    Atul Kr. Ojha

  4. DSI, NUIG, Galway, Ireland

    Atul Kr. Ojha

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This article is part of the topical collection “Social Media Analytics and its Evaluation” guest edited by Thomas Mandl, Sandip Modha, and Prasenjit Majumder.

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Kumar, R., Lahiri, B. & Ojha, A.K. Aggressive and Offensive Language Identification in Hindi, Bangla, and English: A Comparative Study. SN COMPUT. SCI. 2, 26 (2021). https://doi.org/10.1007/s42979-020-00414-6

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