Abstract
In today’s world, internet is an emerging technology with exponential user growth. A major concern with that is the increase of toxic online content by people of different backgrounds. With the expansion of deep learning, quite a lot of researches have inclined toward using their deep neural networks for abundant discipline. Even for natural language processing (NLP)-based tasks, deep networks, specifically recurrent neural network (RNN), and their types are lately being considered over the traditional shallow networks. This paper addresses the problem of hate speech hovering on social media. We propose an LTSM-based classification system that differentiates between hate speech and offensive language. This system describes a contemporary approach that employs word embeddings with LSTM and Bi-LSTM neural networks for the identification of hate speech on Twitter. The best performing LSTM network classifier achieved an accuracy of 86% with early stopping criterion based on loss function during training.
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
Similar content being viewed by others
References
A. Hern, Facebook, YouTube, Twitter and Microsoft sign EU hate speech code. The Guardian. Accessed 7 June 2016
L. Silva, M. Mondal, D. Correa, F. Benevenuto, I. Weber, Analyzing the targets of hate in online social media. arXiv preprint arXiv:1603.07709 (2016)
Hateful Conduct Policy (2017), https://support.twitter.com/articles/. Accessed Feb 2017
Z. Waseem, 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 (2016), pp. 138–142
A. Jha, R. Mamidi, When does a compliment become sexist? analysis and classification of ambivalent sexism using twitter data, in Proceedings of the Second Workshop on NLP and Computational Social Science (2017), pp. 7–16
A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of tricks for efficient text classification (2016). arXiv preprint arXiv:1607.01759
I. Kwok, Y. Wang, Locate the hate: detecting tweets against blacks, in Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)
P. Burnap, M.L. Williams, 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)
J.H. Park, P. Fung, One-step and two-step classification for abusive language detection on twitter. arXiv preprint arXiv:1706.01206 (2017)
B. Gambäck, U.K. Sikdar, Using convolutional neural networks to classify hate-speech, in Proceedings of the First Workshop on Abusive Language Online, pp. 85–90
M. ElSherief, S. Nilizadeh, D. Nguyen, G. Vigna, E. Belding, Peer to peer hate: hate speech instigators and their targets, in Twelfth International AAAI Conference on Web and Social Media (2018)
P. Badjatiya, S. Gupta, M. Gupta, V. Varma, Deep learning for hate speech detection in tweets, In Proceedings of the 26th International Conference on World Wide Web Companion (International World Wide Web Conferences Steering Committee, 2017), pp. 759–760
M.O. Ibrohim, I. Budi, Multi-label hate speech and abusive language detection in indonesian twitter, in Proceedings of the Third Workshop on Abusive Language Online (2019), pp. 46–57
C.N.D. Santos, I. Melnyk, I. Padhi, Fighting offensive language on social media with unsupervised text style transfer. arXiv preprint arXiv:1805.07685 (2018)
U. Bretschneider, R. Peters, Detecting offensive statements towards foreigners in social media, in Proceedings of the 50th Hawaii International Conference on System Sciences (2017)
T. Davidson, D. Warmsley, M. Macy, I. Weber, Automated hate speech detection and the problem of offensive language. arXiv preprint arXiv:1703.04009 (2017)
B. vanAken, J. Risch, R. Krestel, A. Löser, Challenges for toxic comment classification: an in-depth error analysis. arXiv preprint arXiv:1809.07572 (2018)
N. Djuric, J. Zhou, R. Morris, M. Grbovic, V. Radosavljevic, N. Bhamidipati, Hate speech detection with comment embeddings, in Proceedings of the 24th International Conference on World Wide Web (ACM, 2015), pp. 29–30
V. Basile, C. Bosco, E. Fersini, D. Nozza, V. Patti, F.M.R. Pardo, M. Sanguinetti, 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 (2019), pp. 54–63
S.V. Georgakopoulos, S.K. Tasoulis, A.G. Vrahatis, V.P. Plagianakos, Convolutional neural networks for toxic comment classification, in Proceedings of the 10th Hellenic Conference on Artificial Intelligence (ACM, 2018), p. 35
Z. Zhang, D. Robinson, J. Tepper, Detecting hate speech on twitter using a convolution-gru based deep neural network, in European Semantic Web Conference (Springer, Cham, 2018), pp. 745–760
H. Watanabe, M. Bouazizi, T. Ohtsuki, Hate speech on twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6, 13825–13835 (2018)
C. Nobata, J. Tetreault, A. Thomas, Y. Mehdad, Y. Chang, 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, 2016), pp. 145–153
G.K. Pitsilis, H. Ramampiaro, H. Langseth, Detecting offensive language in tweets using deep learning. arXiv preprint arXiv:1801.04433 (2018)
P. Mathur, R. Shah, R. Sawhney, D. Mahata, Detecting offensive tweets in hindi-english code-switched language, in Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media (2018), pp. 18–26
B. Vandersmissen, Automated detection of offensive language behavior on social networking sites. IEEE Trans. (2012)
P. Mathur, R. Sawhney, M. Ayyar, R. Shah, Did you offend me? classification of offensive tweets in hinglish language, in Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) (2018), pp. 138–148
S. Agarwal, A. Sureka, Using knn and svm based one-class classifier for detecting online radicalization on twitter, in International Conference on Distributed Computing and Internet Technology (Springer, Cham, 2015), pp. 431–442
A.H. Razavi, D. Inkpen, S. Uritsky, S. Matwin, Offensive language detection using multi-level classification, in Canadian Conference on Artificial Intelligence (Springer, Berlin, Heidelberg, 2010), pp. 16–27
G. Xiang, B. Fan, L. Wang, J. Hong, C. Rose, Detecting offensive tweets via topical feature discovery over a large scale twitter corpus, in Proceedings of the 21st ACM international conference on Information and knowledge management (ACM, 2012), pp. 1980–1984
M. Zampieri, S. Malmasi, P. Nakov, S. Rosenthal, N. Farra, & R. Kumar, Semeval-2019 task 6: identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983 (2019)
Z. Xu, S. Zhu, Filtering offensive language in online communities using grammatical relations, in Proceedings of the Seventh Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (2010), pp. 1–10
G. Wiedemann, E. Ruppert, R. Jindal, C. Biemann, Transfer learning from LDA to BiLSTM-CNN for offensive language detection in twitter. arXiv preprint arXiv:1811.02906 (2018)
K. Rother, M. Allee, A. Rettberg, Ulmfit at germeval-2018: a deep neural language model for the classification of hate speech in german tweets, in 14th Conference on Natural Language Processing KONVENS 2018 (2018), p. 113
H. Mubarak, K. Darwish, W. Magdy, Abusive language detection on Arabic social media, in Proceedings of the First Workshop on Abusive Language Online (2017), pp. 52–56
T.G. Almeida, B.À. Souza, F.G. Nakamura, E.F. Nakamura, Detecting hate, offensive, and regular speech in short comments, in Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web (ACM, 2017), pp. 225–228
A. Gaydhani, V. Doma, S. Kendre, L. Bhagwat, Detecting hate speech and offensive language on twitter using machine learning: an n-gram and tfidf based approach. arXiv preprint arXiv:1809.08651 (2018)
T. Gröndahl, L. Pajola, M. Juuti, M. Conti, N. Asokan, All you need is: evading hate speech detection, in Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security (ACM, 2018), pp. 2–12
L. Gao, R. Huang, Detecting online hate speech using context aware models. arXiv preprint arXiv:1710.07395 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Bisht, A., Singh, A., Bhadauria, H.S., Virmani, J., Kriti (2020). Detection of Hate Speech and Offensive Language in Twitter Data Using LSTM Model. In: Jain, S., Paul, S. (eds) Recent Trends in Image and Signal Processing in Computer Vision. Advances in Intelligent Systems and Computing, vol 1124. Springer, Singapore. https://doi.org/10.1007/978-981-15-2740-1_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-2740-1_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2739-5
Online ISBN: 978-981-15-2740-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)