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Persian offensive language detection

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Abstract

With the proliferation of social networks and their impact on human life, one of the rising problems in this environment is the rise in verbal and written insults and hatred. As one of the significant platforms for distributing text-based content, Twitter frequently publishes its users’ abusive remarks. Creating a model that requires a complete collection of offensive sentences is the initial stage in recognizing objectionable phrases. In addition, despite the abundance of resources in English and other languages, there are limited resources and studies on identifying hateful and offensive statements in Persian. In this study, we compiled a 38K-tweet dataset of Persian Hate and Offensive language using keyword-based data selection strategies. A Persian offensive lexicon and nine hatred target group lexicons were gathered through crowdsourcing for this purpose. The dataset was annotated manually so that at least two annotators investigated tweets. In addition, for the purpose of analyzing the effect of used lexicons on language model functionality, we employed two assessment criteria (FPED and pAUCED) to measure the dataset’s potential bias. Then, by configuring the dataset based on the results of the bias measurement, we mitigated the effect of words’ bias in tweets on language model performance. The results indicate that bias is significantly diminished, while less than a hundredth reduced the F1 score.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Notes

  1. SemEval-2022.

  2. TWINT.

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Acknowledgements

This research was in part supported by a grant from the School of Computer Science, Institute for Research in Fundamental Sciences, IPM, Iran (No. CS1402-4-237).

Funding

This research was in part supported by a grant from the School of Computer Science, Institute for Research in Fundamental Sciences, IPM (No. CS1400-4-237).

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Emad Kebriaei, Ali Homayouni, Roghayeh Faraji, Armita Razavi, Azadeh Shakery, Heshaam Faili & Yadollah Yaghoobzadeh

  2. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Azadeh Shakery & Heshaam Faili

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  1. Emad Kebriaei
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  2. Ali Homayouni
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Emad Kebriaei, Ali Homayouni, Roghayeh Faraji and Armita Razavi. The first draft of the manuscript was written by Emad Kebriaei. Reviewing the manuscript and editing is done by Azadeh Shakery, Heshaam Faili and Yadollah Yaghoobzadeh. All authors read and approved the final manuscript.

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Correspondence to Emad Kebriaei.

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Code for data cleaning and analysis is provided as part of the replication package. It is available at https://www.dropbox.com/s/z09fjb84wcaqqvn/HSD.zip?dl=0 for review.

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Editor: Nuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak, and Shuo Wang.

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Appendices

Appendices

Identity Keywords

Full list of identity keywords in Persian, English translation(using Google Translate), and with transliteration are shown in Fig. 7.

Fig. 7
figure 7

Identity keywords in Persian, English (using Google Translate), and transliteration

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Kebriaei, E., Homayouni, A., Faraji, R. et al. Persian offensive language detection. Mach Learn 113, 4359–4379 (2024). https://doi.org/10.1007/s10994-023-06370-5

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