Submit your paper : editorIJETjournal@gmail.com
Paper Title : Predicting Cyberbullying on Social Media
ISSN : 2395-1303
Year of Publication : 2022
MLA Style: -Durga Bhavani,Nithya Konda, BhanuSree ,Gowthami Predicting Cyberbullying on Social Media , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org
APA Style: - Durga Bhavani,Nithya Konda, BhanuSree ,Gowthami Predicting Cyberbullying on Social Media , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org
Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behaviour-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behaviour in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behaviour in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviours. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore
 V. Subrahmanian and S. Kumar, ‘‘Predicting human behavior: The next frontiers,’’ Science, vol. 355, no. 6324, p. 489, 2017.  H. Lauw, J. C. Shafer, R. Agrawal, and A. Ntoulas, ‘‘Homophily in the digital world: A LiveJournal case study,’’ IEEE Internet Comput., vol. 14, no. 2, pp. 15–23, Mar./Apr. 2010.  M. A. Al-Garadi, K. D. Varathan, and S. D. Ravana, ‘‘Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network,’’ Comput. Hum. Behav., vol. 63, pp. 433–443, Oct. 2016.  L. Phillips, C. Dowling, K. Shaffer, N. Hodas, and S. Volkova, ‘‘Using social media to predict the future: A systematic literature review,’’ 2017, arXiv:1706.06134. [Online]. Available: https://arxiv.org/abs/1706.06134  H. Quan, J. Wu, and Y. Shi, ‘‘Online social networks & social network services: A technical survey,’’ in Pervasive Communication Handbook. Boca Raton, FL, USA: CRC Press, 2011, p. 4.  J. K. Peterson and J. Densley, ‘‘Is social media a gang? Toward a selection, facilitation, or enhancement explanation of cyber violence,’’ Aggression Violent Behav., 2016.  BBC. (2012). Huge Rise in Social Media. [Online]. Available: http://www.bbc.com/news/uk-20851797  P. A. Watters and N. Phair, ‘‘Detecting illicit drugs on social media using automated social media intelligence analysis (ASMIA),’’ in Cyberspace Safety and Security. Berlin, Germany: Springer, 2012, pp. 66–76.  M. Fire, R. Goldschmidt, and Y. Elovici, ‘‘Online social networks: Threats and solutions,’’ IEEE Commun. Surveys Tuts., vol. 16, no. 4, pp. 2019–2036, 4th Quart., 2014.  N. M. Shekokar and K. B. Kansara, ‘‘Security against sybil attack in social network,’’ in Proc. Int. Conf. InSf. Commun. Embedded Syst. (ICICES), 2016, pp. 1–5.
— Convolutional Neural Networks(CNN), Plant Diseases Detection, Precision agriculture, Deep learning.