Detecting Cyber Bullies on Twitter using Machine Learning Techniques
Author(s): MURALI, Sashaank Pejathaya
Author(s) keywords: cyber bullying detection, data mining, Feature extraction, machine learning algorithms, Twitter
Reference keywords: cyber bullying, cybercrime
Abstract:
The rising use of social networks leads to huge amount of user-generated data. Due to the popularity of social media cyber bullying has become a major problem. Cyber bullying may cause many serious impacts on a person’s life. In the existing system the set of unique features are derived from Twitter such as activity, user and tweet contents. By using these features the cyber bullying words which are presented in the tweets’ content are detected using machine learning algorithms such as Naïve Bayes and Random Forest classifiers. In the proposed work the detection of cyber bully words are integrated into a single unit. The name, gender and age of the cyber bullies will also be detected using feature extraction techniques. In this paper, the Naïve Bayes and Random Forest classifiers are used to detect cyber bullying content that is present in the tweets.
References:
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Article Title: Detecting Cyber Bullies on Twitter using Machine Learning Techniques
Author(s): MURALI, Sashaank Pejathaya
Date of Publication: 2017-06-29
Publication: International Journal of Information Security and Cybercrime
ISSN: 2285-9225 e-ISSN: 2286-0096
Digital Object Identifier: 10.19107/IJISC.2017.01.07
Issue: Volume 6, Issue 1, Year 2017
Section: Cyber-Attacks Evolution and Cybercrime Trends
Page Range: 63-66 (4 pages)
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