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Detection of hateful twitter users with graph convolutional network model

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A Correction to this article was published on 18 December 2023

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Abstract

Today, hate speech is widespread and persistent in various forms on social networking platforms, targeting different minority groups. These attacks can be carried out using various factors such as racial, religious, gender, and physical disability, etc. Considering the number of people and their interactions, social networks are the most important channels through which these discourses spread. The social network structure is considered a set of nodes and edges and is very suitable for the graph structure. The multidimensional structure of social networks carries social network data from Euclidean space to non-Euclidean space. In non-Euclidean space, the graph structure is used to represent data effectively. In this respect, solving the hate speech problem with graph-based methods in a complex dimensional space can produce more impressive results. In this study, a powerful method based on the Graph Convolutional Network (GCN) model, which is rarely used in this field, was proposed for the detection of hateful Twitter users in social networks. Well-known machine learning methods were used to measure the performance of this method. According to the results obtained, the proposed GCN model gave the most successful result.

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Data availability

Program codes available on request and the datasets generated during and/or analyzed during the current study are available in the Kaggle repository and persistent web links to datasets are below:

https://www.kaggle.com/datasets/manoelribeiro/hateful-users-on-twitter

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References

  • Abro S, Shaikh S, Khand ZH, Zafar A, Khan S, Mujtaba G (2020) Automatic hate speech detection using machine learning: a comparative study. Int J Adv Comput Sci Appl 11(8)

  • Al-Hassan A, Al-Dossari H (2019) Detection of hate speech in social networks: a survey on multilingual corpus. In: 6th international conference on computer science and information technology, vol 10, pp 10–5121. https://doi.org/10.5121/csit.2019.90208

  • Albadi N, Kurdi M, Mishra S (2018) Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp 69–76. https://doi.org/10.1109/ASONAM.2018.8508247

  • Antoniadis A, Lambert-Lacroix S, Poggi JM (2021) Random forests for global sensitivity analysis: a selective review. Reliab Eng Syst Saf 206:107312. https://doi.org/10.1016/j.ress.2020.107312

    Article  Google Scholar 

  • Area S, Mesra R (2012) Analysis of Bayes, neural network and tree classifier of classification technique in data mining using WEKA. Comput Sci Inf Technol. https://doi.org/10.5121/csit.2012.2236

    Article  Google Scholar 

  • Badjatiya P, Gupta S, Gupta M, Varma V (2017) Deep learning for hate speech detection in tweets. In: Proceedings of the 26th international conference on world wide web companion, pp 759–760. https://doi.org/10.1145/3041021.3054223

  • Biau G, Scornet E (2016) A random forest guided tour. Test 25(2):197–227. https://doi.org/10.1007/s11749-016-0481-7

    Article  Google Scholar 

  • Bölücü N, Canbay P (2021) Hate speech and offensive content identification with graph convolutional networks. In: Forum for information retrieval evaluation (working notes) (FIRE), CEUR-WS.org.

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv preprint arXiv :1312.6203

  • Burnap P, Williams ML (2015) Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7(2):223–242. https://doi.org/10.1002/poi3.85

    Article  Google Scholar 

  • Cao R, Lee RK, Hoang TA (2020) DeepHate: hate speech detection via multi-faceted text representations. In: 12th ACM conference on web science, pp 11–20. https://doi.org/10.1145/3394231.3397890

  • Chung FR (1997) Spectral graph theory. American Mathematical Soc.

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  • Dukic D, Krzic AS (2021) Detection of hate speech spreaders with BERT. InCLEF (Working Notes), pp 1910–1919

  • Friedkin NE (1978) University social structure and social networks among scientists. Am J Sociol 3(6):1444–1465

    Article  Google Scholar 

  • Garain A, Basu A (2019) The titans at SemEval-2019 task 5: detection of hate speech against immigrants and women in twitter. In: Proceedings of the 13th international workshop on semantic evaluation, pp. 494–497. https://doi.org/10.18653/v1/S19-2088

  • Gitari ND, Zuping Z, Damien H, Long J (2015) A lexicon-based approach for hate speech detection. Int J Multimedia Ubiquitous Eng 10(4):215–230

    Article  Google Scholar 

  • Gröndahl T, Pajola L, Juuti M, Conti M, Asokan N (2018) All you need is" love" evading hate speech detection. In: Proceedings of the 11th ACM workshop on artificial intelligence and security, pp 2–12. https://doi.org/10.1145/3270101.3270103

  • Guo K, Hu Y, Sun Y, Qian S, Gao J, Yin B (2021) Hierarchical graph convolution network for traffic forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, No 1, pp 151–159

  • Jang B, Kim M, Harerimana G, Kang SU, Kim JW (2020) Bi-LSTM model to increase accuracy in text classification: combining Word2vec CNN and attention mechanism. Appl Sci 10(17):5841. https://doi.org/10.3390/app10175841

    Article  Google Scholar 

  • Knoke D, Yang S (2019) Social network analysis. California, USA

  • Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: J. International Conference on Learning Representations

  • Luu QH, Lau MF, Ng SP, Chen TY (2021) Testing multiple linear regression systems with metamorphic testing. J Syst Softw 182:111062. https://doi.org/10.1016/j.jss.2021.111062

    Article  Google Scholar 

  • Ma J, Nervo G, Zheng J (2019) Improving detection of hateful users on twitter using attention and ReFex. CS224W, Stanford, CA

  • MacAvaney S, Yao HR, Yang E, Russell K, Goharian N, Frieder O (2019) Hate speech detection: challenges and solutions. PloS one 14(8):e0221152. https://doi.org/10.1371/journal.pone.0221152

    Article  Google Scholar 

  • Mishra P, Del Tredici M, Yannakoudakis H, Shutova E (2019) Abusive language detection with graph convolutional networks. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, pp 2145–2150

  • Most popular social networks worldwide as of January 2022, ranked by number of monthly active users. Available online: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/. Accessed 25 Sept 2022

  • Mulki H, Haddad H, Ali CB, Alshabani H (2019) L-hsab: A levantine twitter dataset for hate speech and abusive language. In: Proceedings of the third workshop on abusive language online, pp 111–118. https://doi.org/10.18653/v1/W19-3512

  • Murray C (2008) Schools and social networking: Fear or education. Synergy Perspect: Local 6(1):8–12

    Google Scholar 

  • Naseem U, Razzak I, Eklund PW (2021) A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimedia Tools Appl 80(28):35239–35266. https://doi.org/10.1007/s11042-020-10082-6

    Article  Google Scholar 

  • Nielsen, F. Å. (2011). A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903

  • Nobata C, Tetreault J, Thomas A, Mehdad Y, Chang Y (2016) Abusive language detection in online user content. In: Proceedings of the 25th international conference on world wide web, pp 145–153

  • Okwu MO, Tartibu LK (2021) Artificial neural network. In: Metaheuristic optimization: nature-inspired algorithms swarm and computational intelligence, theory and applications. Springer, Cham, pp 133–145. https://doi.org/10.1007/978-3-030-61111-8_14

  • Pradhan A (2012) Support vector machine-a survey. Int J Emerging Technol Adv Eng 2(8):82–85

    Google Scholar 

  • Peng H, Li J, He Y, Liu Y, Bao M, Wang L, Song Y, Yang Q (2018) Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: Proceedings of the 2018 world wide web conference, pp 1063–1072. https://doi.org/10.1145/3178876.3186005

  • Poletto F, Basile V, Sanguinetti M, Bosco C, Patti V (2021) Resources and benchmark corpora for hate speech detection: a systematic review. Lang Resour Eval 55(2):477–523. https://doi.org/10.1007/s10579-020-09502-8

    Article  Google Scholar 

  • Rangel F, De la Peña Sarracén GL, Chulvi B, Fersini E, Rosso P (2021) Profiling Hate Speech Spreaders on Twitter Task at PAN 2021. InCLEF (Working Notes), pp 1772–1789

  • Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, no 22, pp 41–46

  • Roy PK, Tripathy AK, Das TK, Gao XZ (2020) A framework for hate speech detection using deep convolutional neural network. IEEE Access 8:204951–204962. https://doi.org/10.1109/ACCESS.2020.3037073

    Article  Google Scholar 

  • Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674. https://doi.org/10.1109/21.97458

    Article  Google Scholar 

  • Sarracén GL, Rosso P (2021) Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation. Pers Ubiquitous Comput 27:1–3. https://doi.org/10.1007/s00779-021-01605-5

    Article  Google Scholar 

  • Song YY, Ying LU (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27(2):130. https://doi.org/10.11919/j.issn.1002-0829.215044

    Article  Google Scholar 

  • Watanabe H, Bouazizi M, Ohtsuki T (2018) Hate speech on twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6:13825–13835. https://doi.org/10.1109/ACCESS.2018.2806394

    Article  Google Scholar 

  • Wickramasinghe I, Kalutarage H (2021) Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Comput 25(3):2277–2293. https://doi.org/10.1007/s00500-020-05297-6

    Article  Google Scholar 

  • Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

    Article  Google Scholar 

  • Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Comput Social Networks 6(1):1–23. https://doi.org/10.1186/s40649-019-0069-y

    Article  Google Scholar 

  • Zhang B, Zhang H, Zhao G, Lian J (2020) Constructing a PM2. 5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. Environ Modell Softw 124:104600. https://doi.org/10.1016/j.envsoft.2019.104600

    Article  Google Scholar 

  • Zimmerman S, Kruschwitz U, Fox C (2018) Improving hate speech detection with deep learning ensembles. In: Proceedings of the eleventh international conference on language resources and evaluation, pp 2546–2553

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Authors and Affiliations

  1. Computer Engineering Department, Munzur University, 62000, Tunceli, Turkey

    Anıl Utku & Umit Can

  2. Department of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey

    Serpil Aslan

Authors
  1. Anıl Utku
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  2. Umit Can
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  3. Serpil Aslan
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Contributions

All the authors contributed to the study’s conception, design, data analysis, and writing of the original manuscript. Anıl Utku carried out data curation, methodology, and software development. Umit Can and Serpil Aslan contributed to the original draft’s conceptualization, validation, supervision, rewriting, and editing. All authors read and approved the final manuscript draft.

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Correspondence to Umit Can.

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Communicated by: H. Babaie

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Utku, A., Can, U. & Aslan, S. Detection of hateful twitter users with graph convolutional network model. Earth Sci Inform 16, 329–343 (2023). https://doi.org/10.1007/s12145-023-00940-w

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