Natural Language Processing for Cyberbullying Detection
Keywords:machine learning, natural language processing, cyberbullying
With the development of digital technologies and the popularity of social media, cyberbullying has become a serious public health concern that can lead to increased risk of mental and behavioral health issues or even suicide. Artificial intelligence like machine learning opens a lot of possibilities to combat cyberbullying, e.g. automatic cyberbullying detection. Most recent research focuses on improving performance by developing complex models that demand more resources and time to run. The research uses publicly available datasets without carefully evaluating their feasibility and limitations. This study uses natural language processing (NLP) to evaluate the model performance and examine the difference between fine-grained classification and binary classification as well as assess the feasibility and quality of the publicly available dataset. The results show that simple classifier can also achieve similar performance as that of more complex models if appropriate preprocessing is used, and the publicly available dataset may have limitations and quality issues that researchers should consider when using the data.
M. Mickey. “A 15-year-old boy died by suicide after relentless cyberbullying, and his parents say the Latin School could have done more to stop it.” Internet: https://www.cbsnews.com/chicago/news/15-year-old-boy-cyberbullying-suicide-latin-school-chicago-lawsuit/, Apr. 25, 2022 [Jan. 19, 2023].
A.J. Willingham. “The family of a teen who died by suicide after being outed by cyberbullies is demanding justice.” Internet: https://www.cnn.com/2019/09/30/us/channing-smith-suicide-cyberbullying-tennessee-trnd/index.html, Sep. 30, 2019 [Jan. 19, 2023].
K. Rosenblatt. “Cyberbullying tragedy: New Jersey family to sue after 12-year-old daughter's suicide.” Internet: https://www.nbcnews.com/news/us-news/new-jersey-family-sue-school-district-after-12-year-old-n788506, Aug. 1, 2017 [Jan. 19, 2023].
D. Grau and J. Rybak. “Bullying: Words Can Kill.” Internet: https://www.cbsnews.com/news/bullying-words-can-kill/, Sep. 23, 2013 [Jan. 19, 2023].
Ryan’s Story Presentation LLC. “Ryan’s Story Presentation.” Internet: https://www.ryanpatrickhalligan.org/about, 2022 [Jan. 19, 2023].
E. A. Vogels. “Teens and Cyberbullying 2022.” Internet: https://www.pewresearch.org/internet/2022/12/15/teens-and-cyberbullying-2022/, Dec. 15, 2022 [Apr. 6, 2023].
B. Lobe, A. Velicu, E. Staksrud, S. Chaudron, and R. Di Gioia, How children (10-18) experienced online risks during the Covid-19 lockdown - Spring 2020, EUR 30584 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-29763-5, doi:10.2760/562534, JRC124034..
G. Rosen. “Integrity and Transparency Reports, Third Quarter 2022.” Internet: https://about.fb.com/news/2022/11/integrity-and-transparency-reports-q3-2022/, Nov. 22, 2022 [Feb. 23, 2023].
J. Wang, K. Fu, and C. Lu. “Sosnet: A graph convolutional network approach to fine-grained cyberbullying detection,” in 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 1699-1708.
M. Mahmud, M. Mamun, and A. Abdelgawad. “A deep analysis of textual features based cyberbullying detection using machine learning,” in 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 2022, pp. 166-170.
T.H. Aldhyani, M.H. Al-Adhaileh, and S.N. Alsubari. “Cyberbullying identification system based deep learning algorithms.” Electronics, vol. 11, pp. 3273, Oct. 2022.
T. Ahmed, S. Ivan, M. Kabir, H. Mahmud, and K. Hasan. “Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullying.” Social Network Analysis and Mining, vol. 12, pp. 99, Aug. 2022.
S. Bauman. Cyberbullying: What counselors need to know. Alexandria, VA: American Counseling Association, 2011, pp. 204.
E. Menesini and A. Nocentini. “Cyberbullying definition and measurement: Some critical considerations.” Journal of Psychology, vol. 217, pp. 230-232, Jan. 2009.
N. O’Brien and T. Moules. “Not sticks and stones but tweets and texts: Findings from a national cyberbullying project.” Pastoral Care in Education, vol. 217, pp. 53-65, Mar. 2013.
C.E. Notar, S. Padgett, and J. Roden. “Cyberbullying: A review of the literature.” Universal Journal of Educational Research, vol. 1, pp. 1-9, Jun. 2013.
V. Balakrishnan, S. Khan, and H.R. Arabnia. “Improving cyberbullying detection using Twitter users’ psychological features and machine learning.” Computers & Security, vol. 90, pp. 101710, Mar. 2020.
A. Muneer and S.M. Fati. “A comparative analysis of machine learning techniques for cyberbullying detection on twitter.” Future Internet, vol. 12, pp. 187, Oct. 2020.
J. Wang, K. Fu, and C. Lu. “IEEE Big Data 2020 Cyberbullying Dataset.” Internet: https://drive.google.com/drive/folders/1oB2fan6GVGG83Eog66Ad4wK2ZoOjwu3F?usp=sharing, Aug. 23, 2020 [Oct. 18, 2022].
M. Duggan. “Online Harassment 2017.” Internet: https://www.pewresearch.org/internet/2017/07/11/online-harassment-2017/, Jul. 11, 2017 [Apr. 6, 2023].
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