Manuscript Number : IJSRST2182943

Detection of Cyber Bullying on Social Media using Machine Learning

Authors(3) :-M Sravanthi, G Niharika, V Ramya

In the modern era, the usage of the internet has increased tremendously which in turn has led to the evolution of large amounts of data. Cyber world has its own pros and cons. One of the alarming situations in web 4.0 is cyber bullying, a type of cyber-crime. When bullying occurs online with the aid of technology it is known as cyber bullying. This research paper has surveyed the work done by 30 different researchers on cyber bullying, and elaborated on different methodologies adopted by them for the detection of bullying. Three types of features namely textual, behavioral and demographic features are extracted from the dataset as compared to earlier study over the same dataset where only textual features were considered. Textual features include certain bullying words that if exists within the text may lead to a true outcome for cyber bullying. Personality trait features are extracted for the user if it is involved once in bullying may bully in future too. While demographic features extracted from the dataset include age, gender and location. The system is evaluated through different performance measures for both classifiers used and the performance of the Support Vector Machine classifier is found better than the Bernoulli NB with overall 87.14 accuracy.

Authors and Affiliations

M Sravanthi
Assistant Professor, Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
G Niharika
Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
V Ramya
Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India

Cyber Bullying, Machine Learning, SVM, NLP.

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Publication Details

Published in : Volume 9 | Issue 5 | September-October 2022
Date of Publication : 2022-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 626-629
Manuscript Number : IJSRST2182943
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

M Sravanthi, G Niharika, V Ramya, " Detection of Cyber Bullying on Social Media using Machine Learning", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 9, Issue 5, pp.626-629, September-October-2022.
Journal URL : https://ijsrst.com/IJSRST2182943
Citation Detection and Elimination     |      | |
  • Dani Harsh, Jundong Li, and Huan Liu, “Sentiment Informed Cyberbullying Detection in Social Media” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Spinger, Cham, 2017
  • Dinakar, Karthik, Roi Reichart, and Henry Lieberman. “Modeling the detection of Textual Cyberbullying.” The Social Mobile Web 11.02(2011):11-17
  • K. Dinkar, R. Reichart and H. Liebernman, “Modeling the Detection of Textual Cyberbullying,” MIT. International Conference on Weblog nd Social Media. Barcelona, Spain, 2011.
  • M. Dadvar and F.de Jong. 2012.”Cyberbullying detection:astep toward a safer internet yard”. In Proceedings of the 21st International Conference on World Wide Web(WWW ’12 Companion). ACM, New York, NY, USA, 121-126
  • Sunil B. Mane, Yashwanth Sawant, Saif Kazi, Vaibhav Shinde,“Real Time Sentiment Analysis of Twitter Data Using Hadoop”, International Journal of computer Science and Information Technologies,(3098-3100),Vol.5(3),2014.
  • Riya Suchdev, Pallavi Kotkar,Rahul Ravindran, “twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach”, International Journal of Computer Applications(0975-8887),Volume 103 a No.4, October 2014.
  • J. Xu, K. Jun, X. Zhu, and A. Bellmore, “Learning fromBulling Traces in Social Media,"Proc.2012 Conf.North Am. Chapter Assoc. Comput. Linguist. Hun. Lang. Technol, pp. 656-666,2012
  • S. Hnduja and J. W. Patchin “Cyberbullying: Identification, Prevention, & Response,”Cyberbullying Res. Cent, no. October, pp. 1-9,2018
  • A. saravanaraj, J. I. sheebaassistant, S. Pradeep, and D. Dean, “Automatic Detection of Cyberbullying From Twitter.” IRACST-International J. Comput. Sci. Inf. Technol. Secur., vol. 6, no. 6,pp. 2249-9555,2016.
    • " target="_blank"> BibTeX     |
  • Dani Harsh, Jundong Li, and Huan Liu, “Sentiment Informed Cyberbullying Detection in Social Media” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Spinger, Cham, 2017
  • Dinakar, Karthik, Roi Reichart, and Henry Lieberman. “Modeling the detection of Textual Cyberbullying.” The Social Mobile Web 11.02(2011):11-17
  • K. Dinkar, R. Reichart and H. Liebernman, “Modeling the Detection of Textual Cyberbullying,” MIT. International Conference on Weblog nd Social Media. Barcelona, Spain, 2011.
  • M. Dadvar and F.de Jong. 2012.”Cyberbullying detection:astep toward a safer internet yard”. In Proceedings of the 21st International Conference on World Wide Web(WWW ’12 Companion). ACM, New York, NY, USA, 121-126
  • Sunil B. Mane, Yashwanth Sawant, Saif Kazi, Vaibhav Shinde,“Real Time Sentiment Analysis of Twitter Data Using Hadoop”, International Journal of computer Science and Information Technologies,(3098-3100),Vol.5(3),2014.
  • Riya Suchdev, Pallavi Kotkar,Rahul Ravindran, “twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach”, International Journal of Computer Applications(0975-8887),Volume 103 a No.4, October 2014.
  • J. Xu, K. Jun, X. Zhu, and A. Bellmore, “Learning fromBulling Traces in Social Media,"Proc.2012 Conf.North Am. Chapter Assoc. Comput. Linguist. Hun. Lang. Technol, pp. 656-666,2012
  • S. Hnduja and J. W. Patchin “Cyberbullying: Identification, Prevention, & Response,”Cyberbullying Res. Cent, no. October, pp. 1-9,2018
  • A. saravanaraj, J. I. sheebaassistant, S. Pradeep, and D. Dean, “Automatic Detection of Cyberbullying From Twitter.” IRACST-International J. Comput. Sci. Inf. Technol. Secur., vol. 6, no. 6,pp. 2249-9555,2016.
    • " target="_blank">RIS     |
  • Dani Harsh, Jundong Li, and Huan Liu, “Sentiment Informed Cyberbullying Detection in Social Media” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Spinger, Cham, 2017
  • Dinakar, Karthik, Roi Reichart, and Henry Lieberman. “Modeling the detection of Textual Cyberbullying.” The Social Mobile Web 11.02(2011):11-17
  • K. Dinkar, R. Reichart and H. Liebernman, “Modeling the Detection of Textual Cyberbullying,” MIT. International Conference on Weblog nd Social Media. Barcelona, Spain, 2011.
  • M. Dadvar and F.de Jong. 2012.”Cyberbullying detection:astep toward a safer internet yard”. In Proceedings of the 21st International Conference on World Wide Web(WWW ’12 Companion). ACM, New York, NY, USA, 121-126
  • Sunil B. Mane, Yashwanth Sawant, Saif Kazi, Vaibhav Shinde,“Real Time Sentiment Analysis of Twitter Data Using Hadoop”, International Journal of computer Science and Information Technologies,(3098-3100),Vol.5(3),2014.
  • Riya Suchdev, Pallavi Kotkar,Rahul Ravindran, “twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach”, International Journal of Computer Applications(0975-8887),Volume 103 a No.4, October 2014.
  • J. Xu, K. Jun, X. Zhu, and A. Bellmore, “Learning fromBulling Traces in Social Media,"Proc.2012 Conf.North Am. Chapter Assoc. Comput. Linguist. Hun. Lang. Technol, pp. 656-666,2012
  • S. Hnduja and J. W. Patchin “Cyberbullying: Identification, Prevention, & Response,”Cyberbullying Res. Cent, no. October, pp. 1-9,2018
  • A. saravanaraj, J. I. sheebaassistant, S. Pradeep, and D. Dean, “Automatic Detection of Cyberbullying From Twitter.” IRACST-International J. Comput. Sci. Inf. Technol. Secur., vol. 6, no. 6,pp. 2249-9555,2016.
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