This paper is published in Volume-6, Issue-4, 2020
Area
Machine Learning
Author
Sinchana C.
Co-authors
Sinchana K, Pradyumna C S, Deepika S
Org/Univ
Vidya Vardhaka College of Engineering, Mysore, Karnataka, India
Pub. Date
13 July, 2020
Paper ID
V6I4-1214
Publisher
Keywords
Cyberbullying, Support Vector Machine, K-Nearest Neighbor, NaïVe Bayes, Decision Tree, Neural Network

Citationsacebook

IEEE
Sinchana C., Sinchana K, Pradyumna C S, Deepika S. Detection of Cyberbullying using Machine Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sinchana C., Sinchana K, Pradyumna C S, Deepika S (2020). Detection of Cyberbullying using Machine Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 6(4) www.IJARIIT.com.

MLA
Sinchana C., Sinchana K, Pradyumna C S, Deepika S. "Detection of Cyberbullying using Machine Learning." International Journal of Advance Research, Ideas and Innovations in Technology 6.4 (2020). www.IJARIIT.com.

Abstract

Cyberbullying is a form of bullying in which technology is used as a medium to bully someone. As the new boom of the internet and other social media platforms are increasing, the number of users is also increasing and the main users of social media are mostly teens and young adults. As much as these social media platforms are used for getting new information and for entertainment, it is more prone for bullies to uses these networks as vulnerable to attacks against victims. Due to the increase in cyberbullying on victims, it is in need to develop a suitable method for the detection and prevention of cyberbullying. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilize machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching Textual data. The main objective of this project is to detect cyberbullying by matching both Image and Textual data. The test cases and are used to classify the dataset and detect the bullying. Machine learning techniques are used to efficiently predict and detect cyberbullying.