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What Emotion Is Hate? Incorporating Emotion Information into the Hate Speech Detection Task

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PRICAI 2021: Trends in Artificial Intelligence (PRICAI 2021)

Abstract

Finding ethical, platform-independent, computationally efficient methods of adding contextual information to the hate speech detection task is difficult. Methods that rely only on the text for successful classification are of extreme importance. Emotion information extracted from text has been shown to be effective for sentiment analysis and thus we hypothesize that it could have a potential for hate speech. In this study, we propose several methods of introducing emotions into the task of hate speech detection. Using an emotion lexicon, we counter-fitted pre-trained word embeddings (Word2Vec, GloVe, FastText) and also generated a binary and a weighted emotional embedding vector. These were used as features for classification on four publicly available hate speech datasets. Our results and analysis demonstrate that the inclusion of emotion information especially anger, sadness, disgust, fear are helpful for hate speech detection.

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Notes

  1. 1.

    https://github.com/t-davidson/hate-speech-and-offensive-language.

  2. 2.

    https://dataverse.mpi-sws.org/dataset.xhtml?persistentId=doi:10.5072/FK2/ZDTEMN.

  3. 3.

    https://competitions.codalab.org/competitions/19935.

  4. 4.

    https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset.

References

  1. Alorainy, W., Burnap, P., Liu, H., Javed, A., Williams, M.L.: Suspended accounts: a source of tweets with disgust and anger emotions for augmenting hate speech data sample. In: 2018 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 581–586 (2018)

    Google Scholar 

  2. Bilewicz, M., Soral, W.: Hate speech epidemic. the dynamic effects of derogatory language on intergroup relations and political radicalization. Political Psychol. 41(S1), 3–33 (2020)

    Google Scholar 

  3. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information (2017)

    Google Scholar 

  4. Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Proceedings of the 11th International AAAI Conference on Web and Social Media, pp. 512–515. ICWSM ’17 (2017)

    Google Scholar 

  5. Fortuna, P., Soler, J., Wanner, L.: Toxic, hateful, offensive or abusive? what are we really classifying? an empirical analysis of hate speech datasets. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 6786–6794. ELRA, France (2020)

    Google Scholar 

  6. Founta, A.M., et al.: Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior (2018)

    Google Scholar 

  7. Friedman, M.: A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann. Math. Statist. 11(1), 86–92 (1940)

    Article  MathSciNet  Google Scholar 

  8. Gao, L., Huang, R.: Detecting online hate speech using context aware models. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pp. 260–266. INCOMA Ltd., Bulgaria (2017)

    Google Scholar 

  9. Hill, F., Reichart, R., Korhonen, A.: SimLex-999: evaluating semantic models with (genuine) similarity estimation. Comput. Linguist. 41(4), 665–695 (2015)

    Article  MathSciNet  Google Scholar 

  10. Hovy, D., Fornaciari, T.: Increasing in-class similarity by retrofitting embeddings with demographic information. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 671–677. ACL, Belgium (2018)

    Google Scholar 

  11. Koufakou, A., Scott, J.: Lexicon-enhancement of embedding-based approaches towards the detection of abusive language. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pp. 150–157. ELRA, France (2020)

    Google Scholar 

  12. Kwok, I., Wang, Y.: Locate the hate: detecting tweets against blacks. In: AAAI (2013)

    Google Scholar 

  13. Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 302–308. ACL, Maryland (2014)

    Google Scholar 

  14. Loper, E., Bird, S.: Nltk: the natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1, pp. 63–70. ETMTNLP ’02, ACL, USA (2002)

    Google Scholar 

  15. van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  16. Madukwe, K.J., Gao, X., Xue, B.: A ga-based approach to fine-tuning bert for hate speech detection. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2821–2828 (2020)

    Google Scholar 

  17. Madukwe, K.J., Gao, X.: The thin line between hate and profanity. In: 32nd Australasian Joint Conference on Artificial Intelligence, pp. 344–356. Australia (2019)

    Google Scholar 

  18. Madukwe, K.J., Gao, X., Xue, B.: In data we trust: a critical analysis of hate speech detection datasets. In: Proceedings of the Fourth Workshop on Online Abuse and Harms, pp. 150–161. ACL, Online (2020)

    Google Scholar 

  19. Martins, R., Gomes, M., Almeida, J.J., Novais, P., Henriques, P.: Hate speech classification in social media using emotional analysis. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 61–66 (2018)

    Google Scholar 

  20. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)

    Google Scholar 

  21. Mohammad, S.M.: Word affect intensities. In: Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC). Japan (2018)

    Google Scholar 

  22. Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. CAAGET ’10, ACL, USA (2010)

    Google Scholar 

  23. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Google Scholar 

  24. Mollas, I., Chrysopoulou, Z., Karlos, S., Tsoumakas, G.: Ethos: an online hate speech detection dataset (2020)

    Google Scholar 

  25. Mrkšić, N., et al.: Counter-fitting word vectors to linguistic constraints. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 142–148. ACL, San Diego, California (2016)

    Google Scholar 

  26. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. ACL, Doha, Qatar (2014)

    Google Scholar 

  27. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  28. Plutchik, R.: Chapter 1 - a general psychoevolutionary theory of emotion. In: Theories of Emotion, pp. 3–33. Academic Press (1980)

    Google Scholar 

  29. Rajamanickam, S., Mishra, P., Yannakoudakis, H., Shutova, E.: Joint modelling of emotion and abusive language detection (2020)

    Google Scholar 

  30. Safi Samghabadi, N., Hatami, A., Shafaei, M., Kar, S., Solorio, T.: Attending the emotions to detect online abusive language. In: Proceedings of the Fourth Workshop on Online Abuse and Harms, pp. 79–88. ACL, Online (2020)

    Google Scholar 

  31. Seyeditabari, A., Tabari, N., Gholizade, S., Zadrozny, W.: Emotional embeddings: Refining word embeddings to capture emotional content of words (2019)

    Google Scholar 

  32. Vulić, I.: Injecting lexical contrast into word vectors by guiding vector space specialisation. In: Proceedings of The Third Workshop on Representation Learning for NLP, pp. 137–143. ACL, Melbourne, Australia (2018)

    Google Scholar 

  33. Wieting, J., Bansal, M., Gimpel, K., Livescu, K.: From paraphrase database to compositional paraphrase model and back. Trans. ACL 3, 345–358 (2015)

    Google Scholar 

  34. Yu, L.C., Wang, J., Lai, K.R., Zhang, X.: Refining word embeddings for sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 534–539. ACL, Copenhagen, Denmark (2017)

    Google Scholar 

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Correspondence to Kosisochukwu Judith Madukwe .

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Madukwe, K.J., Gao, X., Xue, B. (2021). What Emotion Is Hate? Incorporating Emotion Information into the Hate Speech Detection Task. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_21

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  • DOI: https://doi.org/10.1007/978-3-030-89363-7_21

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