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Supervised Classifiers to Identify Hate Speech on English and Spanish Tweets

  • Sattam AlmatarnehEmail author
  • Pablo Gamallo
  • Francisco J. Ribadas Pena
  • Alexey Alexeev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11853)

Abstract

Consistently with social and political concern about hatred and harassment through social media, in recent years, automatic hate-speech detection and offensive behavior in social media are gaining a lot of attention. In this paper, we examine the performance of several supervised classifiers in the process of identifying hate speech on Twitter. More precisely, we do an empirical study that analyzes the influence of two types of linguistic features (n-grams, word embeddings) when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Complement Naive Bayes (CNB), Decision Tree (DT), Nearest Neighbors (KN), Random Forest (RF) and Neural Network (NN). The experiments we have carried out show that CNB, SVM, and RF are better than the rest classifiers in English and Spanish languages by taking into account all features.

Keywords

Hate speech Sentiment analysis Linguistic features Classification Supervised machine learning 

Notes

Acknowledgments

Research partially funded by the Spanish Ministry of Economy and Competitiveness through projects \(TIN2017-85160-C2-2-R\), and by the Galician Regional Government under projects ED431C 2018/50.

References

  1. 1.
    Almatarneh, S., Gamallo, P.: Comparing supervised machine learning strategies and linguistic features to search for very negative opinions. Information 10(1), 16 (2019). http://www.mdpi.com/2078-2489/10/1/16CrossRefGoogle Scholar
  2. 2.
    Almatarneh, S., Gamallo, P., Pena, F.J.R.: CiTIUS-COLE at semeval - 2019 task 5: combining linguistic features to identify hate speech against immigrants and women on multilingual tweets. In: The 13th International Workshop on Semantic Evaluation (2019)Google Scholar
  3. 3.
    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, pp. 54–63 (2019)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  5. 5.
    Burnap, P., Williams, M.L.: Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7(2), 223–242 (2015)CrossRefGoogle Scholar
  6. 6.
    Burnap, P., Williams, M.L.: Hate speech, machine classification and statistical modelling of information flows on twitter: interpretation and communication for policy decision making (2014)Google Scholar
  7. 7.
    Chen, Y., Zhou, Y., Zhu, S., Xu, H.: Detecting offensive language in social media to protect adolescent online safety. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, pp. 71–80. IEEE (2012)Google Scholar
  8. 8.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)zbMATHGoogle Scholar
  9. 9.
    Dai, A.M., Olah, C., Le, Q.V.: Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015)
  10. 10.
    Fortuna, P., Nunes, S.: A survey on automatic detection of hate speech in text. ACM Comput. Surv. (CSUR) 51(4), 85 (2018)CrossRefGoogle Scholar
  11. 11.
    Gaydhani, A., Doma, V., Kendre, S., Bhagwat, L.: Detecting hate speech and offensive language on twitter using machine learning: An n-gram and tfidf based approach. arXiv preprint arXiv:1809.08651 (2018)
  12. 12.
    Gitari, N.D., Zuping, Z., Damien, H., Long, J.: A lexicon-based approach for hate speech detection. Int. J. Multimed. Ubiquit. Eng. 10(4), 215–230 (2015)CrossRefGoogle Scholar
  13. 13.
    Greevy, E., Smeaton, A.F.: Classifying racist texts using a support vector machine. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 468–469. ACM (2004)Google Scholar
  14. 14.
    Kwok, I., Wang, Y.: Locate the hate: detecting tweets against blacks. In: Twenty-seventh AAAI Conference on Artificial Intelligence (2013)Google Scholar
  15. 15.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)Google Scholar
  16. 16.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  18. 18.
    Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., Chang, Y.: Abusive language detection in online user content. In: Proceedings of the 25th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 145–153 (2016)Google Scholar
  19. 19.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mac. Learn. Res. 12(Oct), 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of naive Bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 616–623 (2003)Google Scholar
  21. 21.
    Tulkens, S., Hilte, L., Lodewyckx, E., Verhoeven, B., Daelemans, W.: A dictionary-based approach to racism detection in dutch social media. arXiv preprint arXiv:1608.08738 (2016)
  22. 22.
    Unsvåg, E.F., Gambäck, B.: The effects of user features on twitter hate speech detection. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pp. 75–85 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sattam Almatarneh
    • 1
    • 2
    Email author
  • Pablo Gamallo
    • 1
  • Francisco J. Ribadas Pena
    • 2
  • Alexey Alexeev
    • 3
  1. 1.Centro Singular de Investigación en Tecnoloxí­as Intelixentes (CiTIUS)Universidad de Santiago de CompostelaSantiagoSpain
  2. 2.Computer Science DepartmentUniversity of Vigo Escola Superior de Enxeñarí­a Informática, Campus As LagoasOurenseSpain
  3. 3.ITMO UniversitySaint-PetersburgRussia

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