International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

A 1-Gram Sentiment Analysis Algorithm for Detecting Cyberbullying in Online Social Networks


Affiliations
1 Department of Computer Science, University of Nigeria Nsukka, Nigeria
2 Department of Information Technology, University University of Nigeria Nsukka, Nigeria
 

Online social networking (OSN) sites in addition to providing business and recreational opportunities are fast becoming a breeding ground for cyberbullying activities. Cyberbullying is an act of harassing or insulting a person by sending messages that are hurting or threatening in nature using electronic communication. Such messages include threats, harassment, and humiliating messages to victims. Other forms are sexual harassments, sexual predating, etc. Cyberbullying poses threat to the physical and mental health of the victims. In this study, sentiments analysis was used to computationally recognize and categorize the opinions, views, and ideas expressed in a piece of text in social media to determine and establish whether the writer's attitude towards a particular topic, person, or a product is positive or negative. The study adopted both quantitative and qualitative approaches. Posts from Facebook were collected and analyzed. The software developed during the research was able detect the presence of cyberbullying in user contents. Results showed promising ability of the software to detect and suspend cyberbullying contents.

Keywords

Social Networks, Cyberbullying, Sentiment Analysis, Hate Speech.
User
Notifications
Font Size

  • ] ScikitLearn, "Sklearn.svm.SVC scikit-learn 0.17.1 documentation,"2018. [Onlin]. Available: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.

  • ] Fu KW, Chan CH, Ip P. Exploring the relationship between cyberbullying and unnatural child death: an ecological study of twenty-four European countries. BMC pediatrics. 2014 Dec;14(1):1-6.

  • ] W. D. Daelemans and Y. Xia., "Automatic Detection and Prevention of Cyberbullying," The First International Conference on Human and Social Analytics, 2018.

  • ] K. Lee, and D. Z. Sui, ‘Content-Driven Detection of Campaigns in Social Media’. 2015. Available at: lee11cikm.pdf (tamu.edu)

  • ] Geo, Djuric, and F. Benevenuto, ‘Characterizing cyberbullying in Online Social Networks Categories and Subject Descriptors’, pp. 49–6, 2017.

  • ] P. K. Smith, Mahdavi M. and N. Tippett, "Cyberbullying: Its nature and impact in Secondary School pupils," Journal of Child Psychology & Psychiatry, vol. 49, pp. 376-385, 20118.

  • ] R. M. Kowalski, G. W. Giumetti and M. R. Lattanner, “Bullying in the digital age: A Critical review and meta-analysis of cyberbullying research among youth,” Psychological Bulletin, vol. 140, no. 4, pp. 1073–1137, 2018.

  • ] Bolla, Raja Ashok "Crime pattern detection using online social media" 2016. Masters Theses. Paper 732.

  • ] D. Yin, Z. Xue L, .Kontostathis A. and L. Edwards, “Detection of Harassment on Web 2.0,” in Proc. Content Analysis of Web 2.0 Workshop, Madrid, Spain, 2018.

  • ] A. Kontostathis, L. Edwards, and A. Leatherman, "Chat Coder: Toward the Tracking cyberbullying and Categorization of Internet Predators," In Proceedings of Text Mining Workshop 2018 held in conjunction with the Ninth SIAM International Conference on Data.

  • ] Sood A, and Bolla, Raja, “Cyberbullying detection using online social media" 2016. Masters Theses. Paper 732.

  • ] Rahman M, Dadvar, F. and R. B. Trieschnigg, “Improved Cyberbullying detection using gender information,” 12th Dutch-Belgian information retrieval workshop (DIR 2012), pp. 23– 25, 2018.

  • ] Reddy. S., Jamil J. M. Dadvar, D. and F. De Jong, “Improving Cyberbullying detection with user context,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol7814 LNCS, pp. 693–696, 2017.

  • ] Kansara M. K., Dinakar, R. Reichart, and H. Lieberman, “Modeling the Detection of Textual Cyberbullying,” in Proc. IEEE International Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, 2017.

  • ] Dinnakar V. Nobate N. and M. Shekokar, “A Framework for Cyberbullying Detection in Social Network,” International Journal of Current Engineering and Technology, vol. 5, no. 1, pp. 494–498, 2016.

  • ] Pitsilis, Georgios K., Heri Ramampiaro, and HelgeLangseth. "Detecting offensive language in tweets using deep learning." arXiv preprint arXiv:1801.04433 (2018).

  • ] Gao A. K. Reynolds, A. Kontostathis, and L. Edwards, "Using Machine Learning to Detect Cyberbullying," In Proceedings of the 2011 10th Conference on Machine Learnin and Applications Workshops, vol. 2, pp. 241-244, December 2018.

  • ] Sirivanos R. Ahmed .Bashir, H. Hosseinmardi and S. Mishra, “Detection of Cyberbullying Incidents on the Instagram Social Network,” 2016.

  • ] Sakaki H. Lobe Dadvar, Van Kansars, and J. I. Sheeba, 'Online Social Network Bullying Detection Using Intelligence Techniques’, vol. 45, pp. 485–492, 2015.

  • ] Bezzera M. Kontosta D. and R. Peters,' Detecting Online Harassment in Social Networks’, no. Li 2017, pp. 1–14.

  • ] Djuric N, Warner H, Nobata P. Ramampiaro W., and H. Langseth, 'Detecting Offensive Language in Tweets Using Deep Learning', 2015, pp. 1–17.

  • ] Sabastine B, Hovy K, Schwartz B., and J. I. Sheeba, 'Online Social Network Bullying Detection Using Intelligence Techniques’, vol. 45, pp. 485–492, 2015.

  • ] Jensen J. Berg Leanes, -H. Saale, and R. Peters, ‘Detecting Online Harassment in Social Networks’, no. Li 2016, pp. 1–14.

  • ] Wang W. Clark, Grieve M. Samghabadi N., and A. Sprague, 'Detecting Cyberbullying in Social Media', 2017.

  • ] Gamback W, I. McGhee, J. Kontostathis, L. and E. Jakubowski, "Learning to Identify Internet Sexual Predation, “International Journal on Electronic Commerce 2018, vol.15, PP, 103-122, 2011.

  • ] Mahata P., K. Dinakar, R. Picard, and H. Lieberman, “Common sense reasoning for detection, prevention, and mitigation of cyberbullying,” IJCAI International Joint Conference on Artificial Intelligence, vol. 2015-Janua, no. 3, pp. 4168–4172, 2018.

  • ] Hoff, Dianne L., and Sidney N. Mitchell. "Cyberbullying: Causes, effects, and remedies." Journal of Educational Administration, 2009.

  • ] Vazsonyi, A.T., Machackova, H., Sevcikova, A., Smahel, D. and Cerna, A., Cyberbullying in context: Direct and indirect effects by low self-control across 25 European countries. European Journal of Developmental Psychology, 9(2), pp.210-227, 2012.

  • ] John, A., Glendenning, A.C., Marchant, A., Montgomery, P., Stewart, A., Wood, S., Lloyd, K. and Hawton, K., 2018. Self-harm, suicidal behaviours, and cyberbullying in children and young people: Systematic review. Journal of medical internet research, 20(4), p.e129.

  • ] FP STAFF, 14-YEAR-OLD GIRL COMMITS SUICIDE, PARENTS BLAME OBSCENE FB POSTS AVAILABLE AT: FIRSTPOST.COM/INDIA/14-YEAR-OLD GIRL COMMITS SUICIDE, PARENTS BLAME OBSCENE FB POSTS-INDIA NEWS , FIRSTPOST

  • ] Beran, T., & Li, Q. (2007). Cyber-harassment: A study of a new method for an old behavior. Journal of Educational Computing Research, 36(3), 265-277.

  • ] Hinduja, S., & Patchin, J. W. (2018). Cyberbullying: An update and synthesis of the research. In R. L. Heath & M. Palenchar (Eds.), The SAGE Handbook of Risk Communication (pp. 337-350). Sage.

  • ] Li, Q. (2019). Cyberbullying in schools: An examination of preservice teachers’ perceptions. Journal of Educational Computing Research, 57(4), 847-866.

  • ] Sourander, A., Brunstein Klomek, A., Ikonen, M., Lindroos, J., Luntamo, T., Koskelainen, M., ... & Helenius, H. (2010). Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Archives of General Psychiatry, 67(7), 720-728.

  • ] Kaya, Sule, and Bilal Alatas. "A New Hybrid LSTM-RNN Deep Learning Based Racism, Xenomy, and Genderism Detection Model in Online Social Network." International Journal of Advanced Networking and Applications 14.2 (2022): 5318-5328.


Abstract Views: 211

PDF Views: 0




  • A 1-Gram Sentiment Analysis Algorithm for Detecting Cyberbullying in Online Social Networks

Abstract Views: 211  |  PDF Views: 0

Authors

C.N. Udanor
Department of Computer Science, University of Nigeria Nsukka, Nigeria
A.H. Eneh
Department of Information Technology, University University of Nigeria Nsukka, Nigeria
D. Goji
Department of Information Technology, University University of Nigeria Nsukka, Nigeria

Abstract


Online social networking (OSN) sites in addition to providing business and recreational opportunities are fast becoming a breeding ground for cyberbullying activities. Cyberbullying is an act of harassing or insulting a person by sending messages that are hurting or threatening in nature using electronic communication. Such messages include threats, harassment, and humiliating messages to victims. Other forms are sexual harassments, sexual predating, etc. Cyberbullying poses threat to the physical and mental health of the victims. In this study, sentiments analysis was used to computationally recognize and categorize the opinions, views, and ideas expressed in a piece of text in social media to determine and establish whether the writer's attitude towards a particular topic, person, or a product is positive or negative. The study adopted both quantitative and qualitative approaches. Posts from Facebook were collected and analyzed. The software developed during the research was able detect the presence of cyberbullying in user contents. Results showed promising ability of the software to detect and suspend cyberbullying contents.

Keywords


Social Networks, Cyberbullying, Sentiment Analysis, Hate Speech.

References