Prediction of Cyberbullying in Social Media on Twitter Using Logistic Regression

As cases of cyberbullying on social media increase, there is a need for efficient measures to detect the vice. This research aims to establish the application of machine learning algorithms in analyzing text on social media to determine potentially harmful comments using logistic regression. The fir...

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Bibliographic Details
Published in:Journal of Applied Informatics and Computing Vol. 9; no. 4; pp. 1681 - 1686
Main Authors: Prayudani, Santi, Adha, Lilis Tiara, Ariyani, Tika, Lubis, Arif Ridho
Format: Journal Article
Language:English
Published: Politeknik Negeri Batam 07.08.2025
Subjects:
cyberbullying
logistic regression
smote method
twitter
ISSN:2548-6861, 2548-6861
Online Access:Get full text
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Summary:As cases of cyberbullying on social media increase, there is a need for efficient measures to detect the vice. This research aims to establish the application of machine learning algorithms in analyzing text on social media to determine potentially harmful comments using logistic regression. The first and most important research question of this study is to assess the extent to which the model is capable of correctly identifying the comments that contain features of cyberbullying and those that do not. The data set included comments from different social media sites and was preprocessed before further analysis was conducted on it. Exploratory Data Analysis was applied in the study to establish relationships and textual features with bullying behavior. As with any other model, after training and testing the model, the results were analyzed using parameters like precision, precision, gain, and F1 statistics. The outcomes of this study revealed that the use of logistic regression models can give a fairly satisfactory level of accuracy in identifying cyberbullying. In light of this, this study underscores the need to use machine learning algorithms to minimize negative actions in cyberspace.
ISSN:2548-6861
2548-6861
DOI:10.30871/jaic.v9i4.9842