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Machine Learning based Approach for Detection of Cyberbullying Tweets

by Rashi Shah, Srushti Aparajit, Riddhi Chopdekar, Rupali Patil
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 37
Year of Publication: 2020
Authors: Rashi Shah, Srushti Aparajit, Riddhi Chopdekar, Rupali Patil
10.5120/ijca2020920946

Rashi Shah, Srushti Aparajit, Riddhi Chopdekar, Rupali Patil . Machine Learning based Approach for Detection of Cyberbullying Tweets. International Journal of Computer Applications. 175, 37 ( Dec 2020), 52-57. DOI=10.5120/ijca2020920946

@article{ 10.5120/ijca2020920946,
author = { Rashi Shah, Srushti Aparajit, Riddhi Chopdekar, Rupali Patil },
title = { Machine Learning based Approach for Detection of Cyberbullying Tweets },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2020 },
volume = { 175 },
number = { 37 },
month = { Dec },
year = { 2020 },
issn = { 0975-8887 },
pages = { 52-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number37/31695-2020920946/ },
doi = { 10.5120/ijca2020920946 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:33.530332+05:30
%A Rashi Shah
%A Srushti Aparajit
%A Riddhi Chopdekar
%A Rupali Patil
%T Machine Learning based Approach for Detection of Cyberbullying Tweets
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 37
%P 52-57
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's technologically sound world the use of social media is inevitable. Along with benefits of social media there are serious negative impacts as well. An important issue that needs to be addressed here is cyberbullying. An effective solution for resolving this issue is the detection of the cyber-bullying content by Machine Learning. This manuscript aims to put forward ideas regarding cyber-bullying detection on the social media platform twitter. The outcome of this manuscript is that whichever tweet is a bully tweet that is represented by the value 1, thus all the bully tweets are detected. The Twitter dataset is equally distributed into bully and non-bully tweets and fed to different machine learning models. The logistic regression classifier provides accurate classification of bully and non-bully tweets with precision of 91%, recall 94% and F1-score 93%. This work will help curb cyber-bullying, so that the users can stay at bay from victimization.

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Index Terms

Computer Science
Information Sciences

Keywords

Cyber Bullying Machine Learning Natural Language Processing (NLP) Twitter Logistic Regression Bully tweets Non-bully tweets Victimization Classification.

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