Abstract
In later half of the twentieth century, though digital revolution began, social communications evolved within small cultural boundaries, their progress was bound by geo-spatial limitations of traditional communications. With the advent of information communication technologies (ICT), new innovations have transcended the spatial limitations, revolutionizing social networking, and world is becoming smaller, borderless, and a better place. However, together with advancements comes the evil effect of technology. The term Social Media (SM) has taken over our lives globally. Social platforms have become a part of our daily affairs, and the increasing use of these platforms by increasing number of users is generating large amount of user behaviour related data every day. With popularity of social platforms, it has introduced a new form of individual violence behaviour termed as cyberbullying. Cyberbullying has had an adverse effect on human’s life creating severe problems, and at times, individuals have been victimized to attempt suicide. This creates the need for construction of models to detect cyberbullying to safeguard the interest of individuals. This paper provides insights about cyberbullying and the process of detecting cyberbullying using machine learning algorithms. The proposed system extracts the information related to network, users, and tweet contents from Twitter platform. The dataset considered for experimenting includes labelled data.
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References
Sonia L et al. (2011) Risks and safety on the internet: the perspective of European children: full findings and policy implications from the EU kids online survey of 9–16-year olds and their parents in 25 countries.
Tokunaga RS (2010) Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput Human Behav 26.3: 277–287
Bullying statistics. http://www.bullyingstatistics.org/content/cyber-bullying-statistics.html
Annapolis. https://www.annapolis.gov/908/Facts-About-cyberbullying
Chris M (2014) Cyberbullying, trends and tudes.“ NCPC. org
https://sites.google.com/a/cypanthers.org/cease-cyber-bullying/statistics
Mckenna KY, Bargh JA (1999) plan 9 from cyberspace: the implications of the internet for personality and social psychology. Personal Soc Psychol Rev 4(1):57–75
Gross EF, Juvonen J, Gable SL (2002) Internet use and well-being in adolescence. J Soc Issues 58(1):75–90
O’Moore M, Kirkham C (2001) Self-esteem and its relationship to bullying behaviour. Aggressive Behav 27(4):269–283
Fekkes M, Pijpers FIM, Fredriks AM, Vogels T, Verloove-Vanhorick SP (2006) Do bullied children get ill, or do ill children get bullied? a prospective cohort study on the relationship between bullying and health-related symptoms. Pediatrics 117(5):1568–1574
Cowie H (2013) Cyberbullying and its impact on young people’s emotional health and well-being. The Psychiatrist 37(5):167–170
Price M, Dalgleish J (2010) Cyberbullying: experiences, impacts and coping strategies as described by australian young people. Youth Stud Australia 29(2):51–59
Royen V, Kathleen et al. (2015) Automatic monitoring of cyberbullying on social networking sites: from technological feasibility to desirability. Telematics Info 32.1:89–97
Parental Sites (e.g.NetNanny, https://www.netnanny.com/)
What is cyberbullying? https://www.stopbullying.gov/cyberbullying/what-is-it/index.html
Willard NE (2007) In: Cyberbullying and cyberthreats: responding to the challenge of online social aggression, threats, and distress. Research Press, Champaign
Olweus D (1993) Bullying at school: what we know and what we can do Blackwell Publishing. Malden
Amanda L et al. (2015)Â In: Teens, technology and friendship. vol 10. Washington, DC, Pew Research Centre, Book
Lynne E, Kontostathis A (2012) Reclaiming privacy: reconnecting victims of cyberbullying and cyber predation. In: Proceedings of the reconciling privacy with social media workshop, held in conjunction with the 2012 ACM conference on computer supported cooperative work. ACM
Al-Garadi MA, Hussain MR, Khan N, Murtaza G, Nweke HF, Ali I, Mujtaba G, Chiroma H, Khattak HA, Gani A (2019) Predicting cyberbullying on social media in the big data era using machine learning algorithms: review of literature and open challenges. IEEE Access 7:70701–70718
Quan H, Wu J, Shi Y (2011) Online social networks and social network services: a technical survey. In: Pervasive communication handbook. Boca Raton, FL, USA, CRC Press, pp 4
Peterson JK, Densley J (2016) Is social media a gang? toward a selection, facilitation, or enhancement explanation of cyber violence. Aggression Violent Behav
BBC (2012) Huge Rise in Social Media. http://www.bbc.com/news/uk20851797
April E, Demoll D, Edwards L (2020) Detecting cyberbullying activity across platforms. In: 17th international conference on information technology–new generations (ITNG 2020). Springer, Cham, pp 45–50
BigelowJL, Edwards A, Edwards L (2016) Detecting cyberbullying using latent semantic indexing. In: Proceedings of the first international workshop on computational methods for cybersafety. pp 11–14
Dooley JJ, Cross D (2009) Cyberbullying versus face-to-face bullying: a review of the similarities and differences. J Psychol 217:182–188
Slonje R, Smith PK, Frise,” A. The Nature of Cyberbullying, and Strategies for Prevention”, Computers in Human Behaviour,29(1), PP:26–32,2013.
Chikashi N et al. (2016) Abusive language detection in online user content. In: Proceedings of the 25th international conference on world wide web. pp 145–153
Imrul K et al. (2015) The social world of content abusers in community question answering. In: Proceedings of the 24th international conference on world wide web. pp 570–580
Karthik D, Reichart R, Lieberman H (2011) Modeling the detection of textual cyberbullying. In: Fifth international AAAI conference on weblogs and social media
https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets
Cynthia H et al. (2018) Automatic detection of cyberbullying in social media text. PloS One 13.10
https://github.com/apeksha104/Cyberbullying-Detection-in-Tweets
Hugo R et al. (2019) Automatic cyberbullying detection: a systematic review. Comput Human Behav 93:333–345
Vimala B, Khan S, Arabnia HR (2020) Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Comput Secur 90:101710
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Sandesh, A., Asha, H.V., Supriya, P. (2022). Detection of Cyberbullying on Twitter Data Using Machine Learning. In: Shetty, N.R., Patnaik, L.M., Nagaraj, H.C., Hamsavath, P.N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Lecture Notes in Electrical Engineering, vol 790. Springer, Singapore. https://doi.org/10.1007/978-981-16-1342-5_54
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