Abstract
With online social platforms becoming more and more accessible to the common masses, the volume of public utterances on a range of issues, events, and persons etc. has increased profoundly. Though most of the content is a manifestation of personal feelings of the individuals, yet a lot of this content often comprises of hate and offensive speech. Exchange of hate and offensive speech has now become a global phenomenon with increased intolerance among societies. However companies running these social media platforms need to discern and remove such unwanted content. This article focuses on automatic detection of hate and offensive speech from Twitter data by employing both conventional machine learning algorithms as well as deep learning architectures. We conducted extensive experiments on a benchmark 25K Twitter dataset with traditional machine learning algorithms as well as using deep learning architectures. The results obtained by us using deep learning architectures are better than state-of-the-art methods used for hate and offensive speech detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: 11th International AAAI Conference on Web and Social Media., Montreal (2017)
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 - WWW 2016, Montral, Qubec, Canada (2016)
Silva, L.A., Mondal, M., Correa, D., Benevenuto, F., Weber, I.: Analyzing the targets of hate in online social media. In: ICWSM, pp. 687–690 (2016)
Mehdad, Y., Tetreault, J.: Do characters abuse more than words? In: Proceedings of the SIGDIAL 2016 Conference, Los Angeles, USA, pp. 299–303. Association for Computational Linguistics (2016)
Waseem, Z.: Are you a racist or am i seeing things? Annotator influence on hate speech detection on Twitter. In: Proceedings of the First Workshop on NLP and Computational Social Science, Austin, Texas, pp. 138–142. Association for Computational Linguistics (2016)
Wei, X., Lin, H., Yang, L., Yu, Y.: A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information 8(3), 92 (2017)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: EMNLP, vol. 14, pp. 1532–1543 (2014)
Porter, M.F.: An algorithm for suffix stripping. Program 14, 130–137 (1980)
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 (SIGIR 2004), pp. pp. 468–469. ACM, New York (2004). https://doi.org/10.1145/1008992.1009074
Del Vigna, F., Cimino, A., Orletta, F.D., Petrocchi, M., Tesconi, M.: Hate me, hate me not: hate speech detection on Facebook. In: Proceedings of the First Italian Conference on Cyber security, Venice, Italy, pp. 86–95 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746–1751 (2014)
Fortuna, P., Nunes, S.: A survey on automatic detection of hate speech in text. ACM Comput. Surv. (CSUR) 51(4), 85 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wani, A.H., Molvi, N.S., Ashraf, S.I. (2020). Detection of Hate and Offensive Speech in Text. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-44689-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44688-8
Online ISBN: 978-3-030-44689-5
eBook Packages: Computer ScienceComputer Science (R0)