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Automatic classification of participant roles in cyberbullying: Can we detect victims, bullies, and bystanders in social media text?

Published online by Cambridge University Press:  18 November 2020

Gilles Jacobs Open the ORCID record for Gilles Jacobs [Opens in a new window] ,
Cynthia Van Hee  and
Véronique Hoste
Gilles Jacobs*
Affiliation:
Language & Translation Technology Team (LT3), Translating, Interpreting & Communications Department, Ghent University, Ghent, Belgium
Cynthia Van Hee
Affiliation:
Language & Translation Technology Team (LT3), Translating, Interpreting & Communications Department, Ghent University, Ghent, Belgium
Véronique Hoste
Affiliation:
Language & Translation Technology Team (LT3), Translating, Interpreting & Communications Department, Ghent University, Ghent, Belgium
*
*Corresponding author. E-mail: gilles.jacobs@ugent.be
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Abstract

Successful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of the event nor the participants involved. Automatic analysis of participant roles in cyberbullying traces enables targeted bullying prevention strategies. In this paper, we aim to automatically detect different participant roles involved in textual cyberbullying traces, including bullies, victims, and bystanders. We describe the construction of two cyberbullying corpora (a Dutch and English corpus) that were both manually annotated with bullying types and participant roles and we perform a series of multiclass classification experiments to determine the feasibility of text-based cyberbullying participant role detection. The representative datasets present a data imbalance problem for which we investigate feature filtering and data resampling as skew mitigation techniques. We investigate the performance of feature-engineered single and ensemble classifier setups as well as transformer-based pretrained language models (PLMs). Cross-validation experiments revealed promising results for the detection of cyberbullying roles using PLM fine-tuning techniques, with the best classifier for English (RoBERTa) yielding a macro-averaged ${F_1}$-score of 55.84%, and the best one for Dutch (RobBERT) yielding an ${F_1}$-score of 56.73%. Experiment replication data and source code are available at https://osf.io/nb2r3.

Keywords

CyberbullyingBullying participantsText classificationSocial media text
Type
Article
Information
Natural Language Engineering , Volume 28 , Issue 2 , March 2022 , pp. 141 - 166
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© The Author(s), 2020. Published by Cambridge University Press

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References

Agrawal, S. and Awekar, A. (2018). Deep learning for detecting cyberbullying across multiple social media platforms. In European Conference on Information Retrieval, Grenoble, France. Cham, Switzerland: Springer, pp. 141–153.CrossRefGoogle Scholar
Al-Garadi, M.A., Varathan, K.D. and Ravana, S.D. (2016). Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network. Computers in Human Behavior 63, 433–443.CrossRefGoogle Scholar
Baroni, M. and Bernardini, S. (2004). BootCaT: Bootstrapping corpora and terms from the web. In Proceedings of the Fourth International Conference on Language Resources and Evaluation, LREC’04. Lisbon, Portugal: European Language Resources Association (ELRA), pp. 1313–1316.Google Scholar
Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Pardo, F.M.R., Rosso, P. and Sanguinetti, M. (2019). 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. Minneapolis, Minnesota, USA: Association for Computational Linguistics, pp. 54–63.CrossRefGoogle Scholar
Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A. and De Bourdeaudhuij, I. (2014). Cyberbullying on social network sites. An experimental study into bystanders’ behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior 31, 259–271.CrossRefGoogle Scholar
Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A. and De Bourdeaudhuij, I. (2015). ‘Can I afford to help?’ How affordances of communication modalities guide bystanders’ helping intentions towards harassment on social network sites. Behaviour & Information Technology 34(4), 425–435.CrossRefGoogle Scholar
Batista, G.E.A.P.A., Prati, R.C. and Monard, M.C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations Newsletter 6(1), 20–29.CrossRefGoogle Scholar
Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022.Google Scholar
Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G. and Vakali, A. (2017). Mean birds: Detecting aggression and bullying on twitter. In Proceedings of the 2017 ACM on Web Science Conference, WebSci’17. Troy, New York, USA: ACM, pp. 13–22.CrossRefGoogle Scholar
Chen, H., Mckeever, S. and Delany, S.J. (2017). Harnessing the power of text mining for the detection of abusive content in social media. In Advances in Computational Intelligence Systems. Cham, Switzerland: Springer, pp. 187–205.CrossRefGoogle Scholar
Cheng, T. and Wicks, T. (2014). Event detection using twitter: A spatio-temporal approach. PloS ONE 9(6), e97807.CrossRefGoogle ScholarPubMed
Childfocus (2018). Clicksafe: Veilig internetten. www.childfocus.be/nl/preventie/clicksafe-veilig-internetten (accessed 2018-05-14).Google Scholar
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46.CrossRefGoogle Scholar
Cowie, H. (2013). Cyberbullying and its impact on young people’s emotional health and well-being. The Psychiatrist 37(5), 167–170.CrossRefGoogle Scholar
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S. and Singer, Y. (2006). Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551–585.Google Scholar
Dadvar, M. (2014). Experts and Machines United Against Cyberbullying. Phd thesis, University of Twente.Google Scholar
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, 391–407.3.0.CO;2-9>CrossRefGoogle Scholar
Delobelle, P., Winters, T. and Berendt, B. (2020). Robbert: A dutch roberta-based language model. ArXiv pre-print https://arxiv.org/abs/2001.06286.Google Scholar
De Smedt, T. and Daelemans, W. (2012). “Vreselijk mooi!” (“Terribly Beautiful!”): A subjectivity lexicon for dutch adjectives. In Proceedings of the Eight International Conference on Language Resources and Evaluation, LREC’12. Istanbul, Turkey: European Language Resources Association (ELRA), pp. 3568–3572.Google Scholar
DeSmet, A., Bastiaensens, S., Van Cleemput, K., Poels, K., Vandebosch, H. and De Bourdeaudhuij, I. (2012). Mobilizing bystanders of cyberbullying: An exploratory study into behavioural determinants of defending the victim. Annual Review of Cybertherapy and Telemedicine 10, 58–63.Google Scholar
de Vries, W., van Cranenburgh, A., Bisazza, A., Caselli, T., van Noord, G. and Nissim, M. (2019). Bertje: A dutch bert model. ArXiv pre-print https://arxiv.org/abs/1912.09582.Google Scholar
Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. (2019). 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. Minneapolis, Minnesota, US: Association for Computational Linguistics, pp. 4171–4186.Google Scholar
Dinakar, K., Jones, B., Havasi, C., Lieberman, H. and Picard, R. (2012). Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Transactions on Interactive Intelligent Systems 2(3), 18:1–18:30.CrossRefGoogle Scholar
Dinakar, K., Reichart, R. and Lieberman, H. (2011). Modeling the detection of textual cyberbullying. In Proceedings of The Social Mobile Web, volume WS-11-02 of AAAI Workshops. Barcelona, Catalonia, Spain: AAAI, pp. 11–17.Google Scholar
Dooley, J.J. and Cross, D. (2009). Cyberbullying versus face-to-face bullying: A review of the similarities and differences. Journal of Psychology 217, 182–188.Google Scholar
Emmery, C., Verhoeven, B., De Pauw, G., Jacobs, G., Van Hee, C., Lefever, E., Desmet, B., Hoste, V. and Daelemans, W. (2019). Current Limitations in Cyberbullying Detection: On Evaluation Criteria, Reproducibility, and Data Scarcity. ArXiv pre-print https://arxiv.org/abs/1801.05617.Google Scholar
Kids Online, EU. (2014). EU Kids Online: Findings, methods, recommendations.Google Scholar
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R. and Lin, C.-J. (2008). LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874.Google Scholar
Fleiss, J.L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin 76(5), 378–382.CrossRefGoogle Scholar
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F. (2012). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(4), 463–484.CrossRefGoogle Scholar
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J. and Moreno, Y. (2014). Assessing the bias in samples of large online networks. Social Networks 38, 16–27.CrossRefGoogle Scholar
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182.Google Scholar
He, H., Bai, Y., Garcia, E.A. and Li, S. (2008). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of the IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). Hong Kong, China: IEEE, pp. 1322–1328.Google Scholar
Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q. and Mishra, S. (2016). Prediction of cyberbullying incidents in a media-based social network. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM’16. Piscataway, NJ, USA: IEEE Press, pp. 186–192.CrossRefGoogle Scholar
Howard, J. and Ruder, S. (2018). Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1. Melbourne, Australia: Association for Computational Linguistics, pp. 328–339.CrossRefGoogle Scholar
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD04. Seattle, WA, USA: ACM, pp. 168–177.CrossRefGoogle Scholar
Japkowicz, N. and Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis 6(5), 429–449.CrossRefGoogle Scholar
Jijkoun, V. and Hofmann, K. (2009). Generating a non-English subjectivity lexicon: Relations that matter. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 398–405.CrossRefGoogle Scholar
Johnson, J.M. and Khoshgoftaar, T.M. (2019). Survey on deep learning with class imbalance. Journal of Big Data 27(6), 1–54.Google Scholar
Kaltiala-Heino, R., Rimpelä, M., Marttunen, M., Rimpelä, A. and Rantanen, P. (1999). Bullying, depression, and suicidal ideation in finnish adolescents: School survey. BMJ 319(7206), 348–351.CrossRefGoogle ScholarPubMed
Klein, J., Cornell, D. and Konold, T. (2012). Relationships between bullying, school climate, and student risk behaviors. School Psychology Quarterly 27(3), 154.CrossRefGoogle ScholarPubMed
Kudo, T. and Richardson, J. (2018). Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Brussels, Belgium: Association for Computational Linguistics, pp. 66–71.CrossRefGoogle Scholar
Kumar, R., Ojha, A.K., Malmasi, S. and Zampieri, M. (2018). Benchmarking aggression identification in social media. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018). Santa Fe, New Mexico, USA: Association for Computational Linguistics, pp. 1–11.Google Scholar
Landis, J.R. and Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174.CrossRefGoogle ScholarPubMed
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L. and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.Google Scholar
Livingstone, S., Kirwil, L., Ponte, C. and Staksrud, E. (2014). In their own words: What bothers children online? European Journal of Communication 29(3), 271–288.CrossRefGoogle Scholar
Ministre de l’ducation nationale (2018). Non au harcelment. www.nonauharcelement.education.gouv.fr (accessed 14 May 2018).Google Scholar
Mohammad, S., Dunne, C. and Dorr, B. (2009). Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2, EMNLP’09. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 599–608.CrossRefGoogle Scholar
Nahar, V., Al-Maskari, S., Li, X. and Pang, C. (2014). Semi-supervised learning for cyberbullying detection in social networks. In Wang, H. and Sharaf, M.A. (eds), Databases Theory and Applications. Cham, Switzerland: Springer, pp. 160–171.Google Scholar
Nansel, T.R., Overpeck, M., Pilla, R.S., Ruan, W.J., Simons-Morton, B. and Scheidt, P. (2001). Bullying behaviors among us youth: Prevalence and association with psychosocial adjustment. Journal of the American Medical Association 285(16), 2094–2100.CrossRefGoogle ScholarPubMed
Nielsen, F.å. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Rowe M., Stankovic M., Dadzie A.-S. and Hardey M. (eds), Proceedings of the ESWC2011 Workshop on ‘Making Sense of Microposts’: Big things come in small packages, CEUR Workshop Proceedings, vol. 718. Heraklion, Crete: CEUR-WS.org, pp. 93–98.Google Scholar
Olweus, D. (1993). Bullying at School: What We Know and What We Can Do, 2nd Edn. Hoboken, New Jersey, USA: Wiley.Google Scholar
O’Neill, B. and Dinh, T. (2018). The Better Internet for Kids Policy Map: Implementing the European Strategy for a Better Internet for Children in European Member States. Technical report, European Commission, European Schoolnet, EU Kids Online.Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12, 2825–2830.Google Scholar
Pennebaker, J.W., Francis, M.E. and Booth, R.J. (2001). Linguistic Inquiry and Word Count: LIWC 2001. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K. and Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1. New Orleans, Louisiana: Association for Computational Linguistics, pp. 2227–2237.CrossRefGoogle Scholar
Price, M. and Dalgleish, J. (2010). Cyberbullying: Experiences, impacts and coping strategies as described by Australian young people. Youth Studies Australia 29(2), 51–59.Google Scholar
Raisi, E. and Huang, B. (2018). Weakly supervised cyberbullying detection with participant-vocabulary consistency. Social Network Analysis and Mining 8(1), 1–17.CrossRefGoogle Scholar
Rehurek, R. and Sojka, P. (2010). Software framework for topic modelling with large corpora. In The LREC 2010 Workshop on new Challenges for NLP Frameworks. Valletta, Malta: European Language Resources Association (ELRA), pp. 45–50.Google Scholar
Reynolds, K., Kontostathis, A. and Edwards, L. (2011). Using machine learning to detect cyberbullying. In Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops, ICMLA’11. Washington, DC, USA: IEEE Computer Society, pp. 241–244.CrossRefGoogle Scholar
Ross, B.C. (2014). Mutual information between discrete and continuous data sets. PloS One 9(2), e87357.CrossRefGoogle ScholarPubMed
Salmivalli, C. (1999). Participant role approach to school bullying: Implications for interventions. Journal of Adolescence 22(4), 453–459.CrossRefGoogle ScholarPubMed
Salmivalli, C. (2010). Bullying and the peer group: A review. Aggression and Violent Behavior 15(2), 112–120.CrossRefGoogle Scholar
Salmivalli, C., Kärnä, A. and Poskiparta, E. (2011a). Counteracting bullying in finland: The kiva program and its effects on different forms of being bullied. International Journal of Behavioral Development 35(5), 405–411.CrossRefGoogle Scholar
Salmivalli, C., Lagerspetz, K., Björkqvist, K., Österman, K. and Kaukiainen, A. (1996). Bullying as a group process: Participant roles and their relations to social status within the group. Aggressive Behavior 22(1), 1–15.3.0.CO;2-T>CrossRefGoogle Scholar
Salmivalli, C. and Pöyhönen, V. (2012). Cyberbullying in finland. In Cyberbullying in the Global Playground: Research from International Perspectives, pp. 57–72.CrossRefGoogle Scholar
Salmivalli, C., Voeten, M. and Poskiparta, E. (2011b). Bystanders matter: Associations between reinforcing, defending, and the frequency of bullying behavior in classrooms. Journal of Clinical Child & Adolescent Psychology 40(5), 668–676.CrossRefGoogle ScholarPubMed
Schank, R.C. (1975). Conceptual Information Processing. New York, NY, USA: Elsevier Science Inc.Google Scholar
Slonje, R. and Smith, P.K. (2008). Cyberbullying: Another main type of bullying? Scandinavian Journal of Psychology 49(2), 147–154.CrossRefGoogle ScholarPubMed
Smith, P.K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S. and Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry 49(4), 376–385.CrossRefGoogle ScholarPubMed
Sprugnoli, R., Menini, S., Tonelli, S., Oncini, F. and Piras, E. (2018). Creating a WhatsApp dataset to study pre-teen cyberbullying. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2). Brussels, Belgium: Association for Computational Linguistics, pp. 51–59.CrossRefGoogle Scholar
Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S. and Tsujii, J. (2012). brat: A web-based tool for NLP-assisted text annotation. In Proceedings of the Demonstrations Session at EACL 2012. Avignon, France: Association of Computational Linguistics, pp. 102–107.Google Scholar
Stone, P.J., Dunphy, D.C.D., Smith, M.S. and Ogilvie, D.M. (1966). The General Inquirer: A Computer Approach to Content Analysis. Cambridge, Massachusetts, USA: MIT Press.Google Scholar
Sui, J. (2015). Understanding and Fighting Bullying with Machine Learning. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.Google Scholar
Tokunaga, R.S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior 26(3), 277–287.CrossRefGoogle Scholar
Tomkins, S.S. (1978). Script theory: Differential magnification of affects. Nebraska Symposium on Motivation 26, 201–236.Google ScholarPubMed
Tucker, C.E. (2010). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research 51(5), 546–562.CrossRefGoogle Scholar
Van Cleemput, K., Bastiaensens, S., Vandebosch, H., Poels, K., Deboutte, G., DeSmet, A. and De Bourdeaudhuij, I. (2013). Zes jaar onderzoek naar cyberpesten in Vlaanderen, België en daarbuiten: een overzicht van de bevindingen. (Six years of cyberbullying research in Flanders, Belgium and beyond: an overview of the findings.). Technical report, Brussels, Belgium: Friendly Attac, IWT-SBO.Google Scholar
van de Kauter, M., Coorman, G., Lefever, E., Desmet, B., Macken, L. and Hoste, V. (2013). LeTs Preprocess: The multilingual LT3 linguistic preprocessing toolkit. Computational Linguistics in the Netherlands Journal 3, 103–120.Google Scholar
Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., De Pauw, G., Daelemans, W. and Hoste, V. (2018). Automatic detection of cyberbullying in social media text. PLOS ONE 13(10), 1–22.CrossRefGoogle ScholarPubMed
Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W. and Hoste, V. (2015a). Automatic detection and prevention of cyberbullying. In Lorenz P. and Bourret C. (eds), Proceedings of the International Conference on Human and Social Analytics. St. Julians, Malta: IARIA, pp. 13–18.CrossRefGoogle Scholar
Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W. and Hoste, V. (2015b). Detection and fine-grained classification of cyberbullying events. In Angelova G., Bontcheva K. and Mitkov R. (eds), Proceedings of Recent Advances in Natural Language Processing. Hissar, Bulgaria: INCOMA Ltd. Shoumen, pp. 672–680.Google Scholar
Van Hee, C., Verhoeven, B., Lefever, E., De Pauw, G., Daelemans, W. and Hoste, V. (2015c). Guidelines for the Fine-Grained Analysis of Cyberbullying, version 1.0. Technical Report LT3 15-01, LT3, Language and Translation Technology Team–Ghent University.Google Scholar
Van Royen, K., Poels, K. and Vandebosch, H. (2016). Harmonizing freedom and protection: Adolescents’ voices on automatic monitoring of social networking sites. Children and Youth Services Review 64, 35–41.CrossRefGoogle Scholar
Vandebosch, H. and Van Cleemput, K. (2009). Cyberbullying among youngsters: Profiles of bullies and victims. New Media & Society 11(8), 1349–1371.CrossRefGoogle Scholar
Vandebosch, H., Van Cleemput, K., Mortelmans, D. and Walrave, M. (2006). Cyberpesten bij jongeren in Vlaanderen: Een studie in opdracht van het viWTA (Cyberbullying among youngsters in Flanders: a study commissoned by the viWTA). Brussels: viWTA. Technical report, Universities of Antwerp & viWTA.Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, pp. 5998–6008.Google Scholar
Wilson, T., Wiebe, J. and Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT’05. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 347–354.CrossRefGoogle Scholar
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M. and Brew, J. (2019). Huggingface’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771.Google Scholar
Xu, J.-M., Jun, K.-S., Zhu, X. and Bellmore, A. (2012). Learning from bullying traces in social media. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT’12. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 656–666.Google Scholar
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. and Le, Q.V. (2019). XLNet: Generalized autoregressive pretraining for language understanding. ArXiv pre-print https://arxiv.org/abs/1906.08237.Google Scholar
Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A. and Edwards, L. (2009). Detection of harassment on web 2.0. In Proceedings of the Content Analysis in the Web 2.0, vol. 2. Madrid, Spain: CAW, pp. 1–7.Google Scholar
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N. and Kumar, R. (2019a). Predicting the type and target of offensive posts in social media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics, pp. 1415–1420.CrossRefGoogle Scholar
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N. and Kumar, R. (2019b). Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). In Proceedings of the 13th International Workshop on Semantic Evaluation. Minneapolis, Minnesota, USA: Association for Computational Linguistics, pp. 75–86.CrossRefGoogle Scholar
Zhang, T. (2004). Solving large scale linear prediction problems using stochastic gradient descent algorithms. In Proceedings of the Twenty-First International Conference on Machine Learning, 116. New York, NY, USA: ACM.Google Scholar
Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., Hu, H., Luo, F., Macbeth, J. and Dillon, E. (2016). Cyberbullying detection with a pronunciation based convolutional neural network. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA: IEEE, pp. 740–745.CrossRefGoogle Scholar
Zhao, R. and Mao, K. (2017). Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Transactions on Affective Computing 8(3), 328–339.CrossRefGoogle Scholar
Zijlstra, H., Van Meerveld, T., Van Middendorp, H., Pennebaker, J.W. and Geenen, R. (2004). De Nederlandse versie van de ‘linguistic inquiry and word count’ (LIWC). Gedrag Gezond 32, 271–281.Google Scholar