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2021 | OriginalPaper | Chapter

A Survey About the Cyberbullying Problem on Social Media by Using Machine Learning Approaches

Authors : Carlo Sansone, Giancarlo Sperlí

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

The exponential growth of connected devices (i.e. laptops, smartphones or tablets) has radically changed communications means, also making it faster and impersonal by using On-line Social Networks and Instant messaging through several apps. In this paper we discuss about the cyberbullying problem, focusing on the analysis of the state-of-the-art approaches that can be classified in four different tasks (Binary Classification, Role Identification, Severity Score Computation and Incident prediction). In particular, the first task aims to predict if a particular action is aggressive or not based on the analysis of different features. In turn, the second and the third task investigate the cyberbullying problem by identifying users’ role in the exchanged message or assigning a severity score to a given users or session respectively. Nevertheless, information heterogeneity, due to different multimedia contents (i.e. text, emojis, stickers or gifs), and the use of datasets, which are typically unlabeled or manually labelled, create continuous challenges in addressing the cyberbullying problem.

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previous chapter RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety

https://facebook.com/.

https://ask.fm/.

http://www.bullyingstatistics.org/category/bullying-statistics.

http://websites.psychology.uwa.edu.au/school/MRCDatabase/mrc2.html.

https://www.kaggle.com/swetaagrawal/formspring-data-for-cyberbullying-detection.

https://www.chatcoder.com/drupal/index.php.

http://www.cucybersafety.org/home/cyberbullying-detection-project/dataset.

Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Comput. Secur. 101710 (2020)

Calvo-Morata, A., Rotaru, D.C., Alonso-Fernández, C., Freire, M., Martínez-Ortiz, I., Fernández-Manjón, B.: Validation of a cyberbullying serious game using game analytics. IEEE Trans. Learn. Technol. (2018)

Chatzakou, D., et al.: Detecting cyberbullying and cyberaggression in social media. ACM Trans. Web (TWEB) 13(3), 1–51 (2019)CrossRef

Cheng, L., Guo, R., Silva, Y., Hall, D., Liu, H.: Hierarchical attention networks for cyberbullying detection on the Instagram social network. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 235–243. SIAM (2019)

Cheng, L., Li, J., Silva, Y.N., Hall, D.L., Liu, H.: XBully: cyberbullying detection within a multi-modal context. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 339–347 (2019)

Di Capua, M., Di Nardo, E., Petrosino, A.: Unsupervised cyber bullying detection in social networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 432–437, December 2016. https://doi.org/10.1109/ICPR.2016.7899672

Dinakar, K., Jones, B., Havasi, C., Lieberman, H., Picard, R.: Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans. Interact. Intell. Syst. (TiiS) 2(3), 18 (2012)

Farag, N., El-Seoud, S.A., McKee, G., Hassan, G.: Bullying hurts: a survey on non-supervised techniques for cyber-bullying detection. In: Proceedings of the 2019 8th International Conference on Software and Information Engineering, pp. 85–90 (2019)

Farley, S., Coyne, I., D’Cruz, P.: Cyberbullying at work: understanding the influence of technology (2018)

10.

Hosseinmardi, H., Ghasemianlangroodi, A., Han, R., Lv, Q., Mishra, S.: Towards understanding cyberbullying behavior in a semi-anonymous social network. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 244–252. IEEE (2014)

11.

Hosseinmardi, H., et al.: A comparison of common users across Instagram and Ask.fm to better understand cyberbullying. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp. 355–362. IEEE (2014)

12.

Hosseinmardi, H., Mattson, S.A., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Detection of cyberbullying incidents on the Instagram social network. In: Association for the Advancement of Artificial Intelligence (2015)

13.

Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media-based social network. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 186–192. IEEE (2016)

14.

Juvonen, J., Gross, E.F.: Extending the school grounds?-bullying experiences in cyberspace. J. Sch. Health 78(9), 496–505 (2008)CrossRef

15.

Katzer, C., Fetchenhauer, D., Belschak, F.: Cyberbullying: who are the victims? A comparison of victimization in internet chatrooms and victimization in school. J. Media Psychol. 21(1), 25–36 (2009)CrossRef

16.

Kowalski, R.M., Toth, A., Morgan, M.: Bullying and cyberbullying in adulthood and the workplace. J. Soc. Psychol. 158(1), 64–81 (2018)CrossRef

17.

Kshetri, N., Voas, J.: Thoughts on cyberbullying. Computer 52(4), 64–68 (2019)CrossRef

18.

Kumar, A., Sachdeva, N.: Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed. Tools Appl. 78(17), 23973–24010 (2019). https://doi.org/10.1007/s11042-019-7234-zCrossRef

19.

Kumari, K., Singh, J.P., Dwivedi, Y.K., Rana, N.P.: Towards cyberbullying-free social media in smart cities: a unified multi-modal approach. Soft Comput. 1–12 (2019)

20.

Li, Q.: New bottle but old wine: a research of cyberbullying in schools. Comput. Hum. Behav. 23(4), 1777–1791 (2007)CrossRef

21.

Newall, M.: Cyberbullying: A Global Advisor Survey. Ipsos (2018)

22.

Patchin, J.W., Hinduja, S.: Bullies move beyond the schoolyard: a preliminary look at cyberbullying. Youth Violence Juvenile Justice 4(2), 148–169 (2006)CrossRef

23.

Potha, N., Maragoudakis, M.: Time series forecasting in cyberbullying data. In: Iliadis, L., Jayne, C. (eds.) EANN 2015. CCIS, vol. 517, pp. 289–303. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23983-5_27CrossRef

24.

Potha, N., Maragoudakis, M., Lyras, D.: A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data. Knowl.-Based Syst. 96, 134–155 (2016)CrossRef

25.

Raisi, E., Huang, B.: Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 409–416 (2017)

26.

Raisi, E., Huang, B.: Weakly supervised cyberbullying detection using co-trained ensembles of embedding models. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 479–486. IEEE (2018)

27.

Rosa, H., Matos, D., Ribeiro, R., Coheur, L., Carvalho, J.P.: A “deeper” look at detecting cyberbullying in social networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2018. https://doi.org/10.1109/IJCNN.2018.8489211

28.

Salawu, S., He, Y., Lumsden, J.: Approaches to automated detection of cyberbullying: a survey. IEEE Trans. Affect. Comput. (2017)

29.

Schwartz, H.A., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9), e73791 (2013)CrossRef

30.

Singh, V.K., Hofenbitzer, C.: Fairness across network positions in cyberbullying detection algorithms. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 557–559 (2019)

31.

Squicciarini, A., Rajtmajer, S., Liu, Y., Griffin, C.: Identification and characterization of cyberbullying dynamics in an online social network. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 280–285 (2015)

32.

Tomkins, S., Getoor, L., Chen, Y., Zhang, Y.: A socio-linguistic model for cyberbullying detection. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 53–60. IEEE (2018)

33.

Yao, M., Chelmis, C., Zois, D.: Cyberbullying detection on Instagram with optimal online feature selection. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 401–408, August 2018. https://doi.org/10.1109/ASONAM.2018.8508329

34.

Yao, M., Chelmis, C., Zois, D.S.: Cyberbullying ends here: towards robust detection of cyberbullying in social media. In: The World Wide Web Conference, pp. 3427–3433 (2019)

35.

Zhao, R., Mao, K.: Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans. Affect. Comput. 8(3), 328–339 (2016)CrossRef

36.

Zhong, H., et al.: Content-driven detection of cyberbullying on the Instagram social network. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 3952–3958 (2016)

37.

Zych, I., Ortega-Ruiz, R., Del Rey, R.: Systematic review of theoretical studies on bullying and cyberbullying: facts, knowledge, prevention, and intervention. Aggress. Violent Behav. 23, 1–21 (2015)CrossRef

Title: A Survey About the Cyberbullying Problem on Social Media by Using Machine Learning Approaches
Authors: Carlo Sansone
Giancarlo Sperlí
Publisher: Springer International Publishing
Book: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68786-1

Electronic ISBN: 978-3-030-68787-8

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-68787-8_48

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