Abstract
Hate speech detection has received a lot of attention in recent years. However, there are still a number of challenges to monitor hateful content in social media, especially in scenarios with few data. In this paper we propose HaGNN, a convolutional graph neural network that is capable of performing an accurate text classification in a supervised way with a small amount of labeled data. Moreover, we propose Similarity Penalty, a novel loss function that considers the similarity among nodes in the graph to improve the final classification. Particularly, our goal is to overcome hate speech detection in data-poor settings. As a result we found that our model is more stable than other state-of-the-art deep learning models with few data in the considered datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We will make our codes freely available by the publication date of this work.
- 2.
- 3.
References
Basile, V., et al.: Semeval-2019 Task 5: multilingual detection of hate speech against immigrants and women in twitter. In: 13th International Workshop on Semantic Evaluation, pp. 54–63. Association for Computational Linguistics (2019)
Cer, D., Yang, Y., et al.: Universal Sentence Encoder (2018). CoRR abs/1803.11175. http://arxiv.org/abs/1803.11175
Chung, Y.L., Kuzmenko, E., Tekiroglu, S.S., Guerini, M.: CONAN-counter narratives through nichesourcing: a multilingual dataset of responses to fight online hate speech. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, vol. 1 (Long Papers), pp. 2819–2829. Association for Computational Linguistics, 28 July-2 August 2019 (2019)
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, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://www.aclweb.org/anthology/N19-1423
Fortuna, P., Nunes, S.: A survey on automatic detection of hate speech in text. ACM Comput. Surv. (CSUR) 51(4), 1–30 (2018)
Isaksen, V., Gambäck, B.: Using transfer-based language models to detect hateful and offensive language online. In: Proceedings of the Fourth Workshop on Online Abuse and Harms, pp. 16–27 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations, ICLR 2017 (2017)
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. In: International Conference on Learning Representations (2020), https://openreview.net/forum?id=H1eA7AEtvS
Pezzotti, N., Lelieveldt, B.P.F., Maaten, L.v.d., Höllt, T., Eisemann, E., Vilanova, A.: Approximated and user steerable tSNE for progressive visual analytics. IEEE Trans. Visual. Comput. Graphics 23(7), 1739–1752 (2017). https://doi.org/10.1109/TVCG.2016.2570755
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2021). https://doi.org/10.1109/TNNLS.2020.2978386
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370–7377 (2019)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
Acknowledgements
This research work was partially funded by the Spanish Ministry of Science and Innovation under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The first author gratefully acknowledges the support of the Pro\(^2\)Haters - Proactive Profiling of Hate Speech Spreaders (CDTi IDI-20210776) and XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy detection during infodemics (MICIN PLEC2021-007681) R&D grants. The work of the first author was also partially funded by the Centre for the Development of Industrial Technology (CDTI) of the Spanish Ministry of Science and Innovation under the research project IDI-20210776 on Proactive Profiling of Hate Speech Spreaders - PROHATER (Perfilador Proactivo de Difusores de Mensajes de Odio). Moreover, the work of the second author was partially funded by the Generalitat Valenciana under DeepPattern (PROMETEO/2019/121).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
De la Peña Sarracén, G.L., Rosso, P. (2022). Convolutional Graph Neural Networks for Hate Speech Detection in Data-Poor Settings. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham. https://doi.org/10.1007/978-3-031-08473-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-08473-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08472-0
Online ISBN: 978-3-031-08473-7
eBook Packages: Computer ScienceComputer Science (R0)