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BERT Model-Based Approach for Detecting Racism and Xenophobia on Twitter Data

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Metadata and Semantic Research (MTSR 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1537))

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Abstract

The large amount of data generated on social networks makes the task of moderating textual content written by users complex and impossible to do manually. One of the most prominent problems on social networks is racism and xenophobia. Although there are studies of predictive models that make use of natural language processing techniques to detect racist or xenophobic texts, a lack of these has been observed in the Spanish language. In this paper we present a solution based on deep learning models and, more specifically, models based on transfer learning to detect racist and xenophobic messages in Spanish. For this purpose, a dataset obtained from the social network Twitter has been created using data mining techniques and, after a preprocessing, it has been labelled into racist messages and non-racist messages. The trained models are based on BERT and were called BETO and mBERT. Promising results were obtained showing 85.14% accuracy in the best performing model.

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References

  1. Ahmad, M., Aftab, S., Bashir, M.S., Hameed, N.: Sentiment analysis using SVM: a systematic literature review. Int. J. Adv. Comput. Sci. Appl. 9(2), 182–188 (2018). https://doi.org/10.14569/IJACSA.2018.090226

    Article  Google Scholar 

  2. Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus. In: Computer Science & Information Technology (CS & IT), pp. 83–100. AIRCC Publishing Corporation, February 2019. https://doi.org/10.5121/csit.2019.90208

  3. Alotaibi, A., Abul Hasanat, M.H.: Racism detection in Twitter using deep learning and text mining techniques for the Arabic language. In: Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020, pp. 161–164 (2020). https://doi.org/10.1109/SMART-TECH49988.2020.00047

  4. Anonymous: Finsbury Park attack: son of hire boss held over Facebook post. BBC News (2017). https://www.bbc.co.uk/news/uk-wales-40347813/

  5. del Arco, F.M.P., Molina-González, M.D., Ureña-López, L.A., Martín-Valdivia, M.T.: Comparing pre-trained language models for Spanish hate speech detection. Expert Syst. Appl. 166, 114120 (2021). https://doi.org/10.1016/j.eswa.2020.114120, https://www.sciencedirect.com/science/article/pii/S095741742030868X

  6. Barlett, C.P.: Anonymously hurting others online: the effect of anonymity on cyberbullying frequency. Psychol. Pop. Media Cult. 4(2), 70–79 (2015). https://doi.org/10.1037/a0034335

    Article  Google Scholar 

  7. Basile, V., et al.: SemEval-2019 task 5: multilingual detection of hate speech against immigrants and women in Twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA, pp. 54–63. Association for Computational Linguistics, June 2019. https://doi.org/10.18653/v1/S19-2007

  8. Bisht, A., Singh, A., Bhadauria, H.S., Virmani, J., Kriti: Detection of hate speech and offensive language in Twitter data using LSTM model, pp. 243–264. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2740-1_17

  9. Br Ginting, P.S., Irawan, B., Setianingsih, C.: Hate speech detection on Twitter using multinomial logistic regression classification method. In: 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), pp. 105–111 (2019). https://doi.org/10.1109/IoTaIS47347.2019.8980379

  10. Cañete, J., Chaperon, G., Fuentes, R., Ho, J.H., Kang, H., Pérez, J.: Spanish pre-trained BERT model and evaluation data. In: PML4DC at ICLR 2020 (2020)

    Google Scholar 

  11. Chaudhry, I.: Hashtagging hate: using Twitter to track racism online. First Monday, vol. 20, no. 2 (2015). https://doi.org/10.5210/fm.v20i2.5450https://journals.uic.edu/ojs/index.php/fm/article/view/5450

  12. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1023/A:1022627411411

    Article  MATH  Google Scholar 

  13. Criss, S., Michaels, E.K., Solomon, K., Allen, A.M., Nguyen, T.T.: Twitter fingers and echo chambers: exploring expressions and experiences of online racism using Twitter. J. Racial Ethn. Health Disparities 8(5), 1322–1331 (2020). https://doi.org/10.1007/s40615-020-00894-5

    Article  Google Scholar 

  14. Del Vigna, F., Cimino, A., Dell’Orletta, F., Petrocchi, M., Tesconi, M.: Hate me, hate me not: hate speech detection on Facebook. CEUR Workshop Proc. 1816, 86–95 (2017)

    Google Scholar 

  15. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics, June 2019. https://doi.org/10.18653/v1/N19-1423

  16. de los diputados, C., Government, S., October 2020. https://www.parlamento2030.es/initiatives/3381886de6b06a9ab93ac0bed74cbc61d9259c1c

  17. Garcia, K., Berton, L.: Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Appl. Soft Comput. 101, 107057 (2021). https://doi.org/10.1016/j.asoc.2020.107057, https://www.sciencedirect.com/science/article/pii/S1568494620309959

  18. García Nieto, P.J., García-Gonzalo, E., Paredes-Sánchez, J.P., Bernardo Sánchez, A., Menéndez Fernández, M.: Predictive modelling of the higher heating value in biomass torrefaction for the energy treatment process using machine-learning techniques. Neural Comput. Appl. 31(12), 8823–8836 (2019). https://doi.org/10.1007/s00521-018-3870-x

    Article  Google Scholar 

  19. Hasan, M.R., Maliha, M., Arifuzzaman, M.: Sentiment analysis with NLP on Twitter data. In: 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), pp. 1–4 (2019). https://doi.org/10.1109/IC4ME247184.2019.9036670

  20. Kalaivani, A., Thenmozhi, D.: SSN_NLP_MLRG at SemEval-2020 task 12: offensive language identification in English, Danish, Greek using BERT and machine learning approach. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, Barcelona, pp. 2161–2170. International Committee for Computational Linguistics (online), December 2020. https://aclanthology.org/2020.semeval-1.287

  21. Kumar, P., Singh, A., Kumar, P., Kumar, C.: An explainable machine learning approach for definition extraction. In: Bhattacharjee, A., Borgohain, S.K., Soni, B., Verma, G., Gao, X.-Z. (eds.) MIND 2020. CCIS, vol. 1241, pp. 145–155. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-6318-8_13

    Chapter  Google Scholar 

  22. Lakshmi, R., Divya, S.R.B., Valarmathi, R.: Analysis of sentiment in Twitter using logistic regression. Int. J. Eng. Technol. 7(233), 619–621 (2018). https://doi.org/10.14419/ijet.v7i2.33.14849

    Article  Google Scholar 

  23. Menéndez García, L.A., Sánchez Lasheras, F., García Nieto, P.J., Álvarez de Prado, L., Bernardo Sánchez, A.: Predicting benzene concentration using machine learning and time series algorithms. Mathematics 8(12), 2205 (2020). https://doi.org/10.3390/math8122205

  24. Nedjah, N., Santos, I., de Macedo Mourelle, L.: Sentiment analysis using convolutional neural network via word embeddings. Evol. Intell. (2019). https://doi.org/10.1007/s12065-019-00227-4

  25. Paetzold, G.H., Zampieri, M., Malmasi, S.: UTFPR at SemEval-2019 task 5: hate speech identification with recurrent neural networks. In: Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA, pp. 519–523. Association for Computational Linguistics, June 2019. https://doi.org/10.18653/v1/S19-2093

  26. Pereira-Kohatsu, J.C., Quijano-Sánchez, L., Liberatore, F., Camacho-Collados, M.: Detecting and monitoring hate speech in Twitter. Sensors 19(21) (2019). https://doi.org/10.3390/s19214654

  27. Plaza-Del-Arco, F.M., Molina-González, M.D., Ureña López, L.A., Martín-Valdivia, M.T.: Detecting misogyny and xenophobia in Spanish tweets using language technologies. ACM Trans. Internet Technol. 20(2) (2020). https://doi.org/10.1145/3369869

  28. Rastogi, S., Bansal, D.: Visualization of Twitter sentiments on Kashmir territorial conflict. Cybern. Syst. 52, 642–669 (2021). https://doi.org/10.1080/01969722.2021.1949520

    Article  Google Scholar 

  29. Rodríguez Maeso, S.: “Europe’’ and the narrative of the “true racist’’: (un-)thinking anti-discrimination law through race. Oñati Socio-Legal Ser. 8(6), 845–873 (2018). https://doi.org/10.35295/osls.iisl/0000-0000-0000-0974

    Article  Google Scholar 

  30. Roy, P.K., Tripathy, A.K., Das, T.K., Gao, X.: A framework for hate speech detection using deep convolutional neural network. IEEE Access 8, 204951–204962 (2020)

    Article  Google Scholar 

  31. Saha, B.N., Senapati, A., Mahajan, A.: LSTM based deep RNN architecture for election sentiment analysis from Bengali newspaper. In: 2020 International Conference on Computational Performance Evaluation (ComPE), pp. 564–569 (2020). https://doi.org/10.1109/ComPE49325.2020.9200062

  32. Sayan, P.: Enforcement of the anti-racism legislation of the European Union against antigypsyism. Ethnic Racial Stud. 42(5), 763–781 (2019). https://doi.org/10.1080/01419870.2018.1468568

    Article  Google Scholar 

  33. Singh, M., Bansal, D., Sofat, S.: Who is who on Twitter-spammer, fake or compromised account? A tool to reveal true identity in real-time. Cybern. Syst. 49(1), 1–25 (2018). https://doi.org/10.1080/01969722.2017.1412866

    Article  Google Scholar 

  34. Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune BERT for text classification? In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds.) CCL 2019. LNCS (LNAI), vol. 11856, pp. 194–206. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32381-3_16

    Chapter  Google Scholar 

  35. Talita, A., Wiguna, A.: Implementasi algoritma long short-term memory (LSTM) untuk mendeteksi ujaran kebencian (hate speech) pada kasus pilpres 2019. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 19(1), 37–44 (2019). https://doi.org/10.30812/matrik.v19i1.495

  36. Travis, A.: Anti-Muslim hate crime surges after Manchester and London bridge. The Guardian (2017). https://www.theguardian.com/society/2017/jun/20/anti-muslim-hate-surges-after-manchester-and-london-bridge-attacks

  37. Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

  38. Watanabe, H., Bouazizi, M., Ohtsuki, T.: Hate speech on Twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6, 13825–13835 (2018). https://doi.org/10.1109/ACCESS.2018.2806394

    Article  Google Scholar 

  39. Zhang, Z., Luo, L.: Hate speech detection: a solved problem? The challenging case of long tail on Twitter. Semantic Web 10(5), 925–945 (2019). https://doi.org/10.3233/SW-180338

    Article  Google Scholar 

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Funding

This research was funded by the Junta de Castilla y León grant number LE014G18.

Author information

Authors and Affiliations

  1. SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071, León, Spain

    José Alberto Benitez-Andrades & Carmen Benavides

  2. Universidad de León, Campus of Vegazana s/n, 24071, León, Spain

    Álvaro González-Jiménez & Álvaro López-Brea

  3. SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24071, León, Spain

    Jose Aveleira-Mata, José-Manuel Alija-Pérez & María Teresa García-Ordás

Authors
  1. José Alberto Benitez-Andrades
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  3. Álvaro López-Brea
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Correspondence to José Alberto Benitez-Andrades .

Editor information

Editors and Affiliations

  1. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Complutense University of Madrid, Madrid, Spain

    María-Antonia Ovalle-Perandones

  3. University College London, London, UK

    Andreas Vlachidis

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Benitez-Andrades, J.A. et al. (2022). BERT Model-Based Approach for Detecting Racism and Xenophobia on Twitter Data. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham. https://doi.org/10.1007/978-3-030-98876-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-98876-0_13

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