A Machine Learning Ensemble Model for the Detection of Cyberbullying

TL;DR

This paper presents a stacking ensemble machine learning model that effectively detects cyberbullying in social media posts, achieving high accuracy and outperforming previous methods on the same dataset.

Contribution

The study introduces a novel stacking ensemble approach combining multiple features and classifiers for improved cyberbullying detection accuracy.

Findings

Achieved 94% accuracy in detecting aggressive tweets.

Outperformed traditional machine learning models and previous experiments.

Demonstrated the effectiveness of ensemble methods in cyberbullying detection.

Abstract

The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms. Motivated by this necessity, we present this paper to contribute to developing an automated system for detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to previous experiments on the same dataset. We employed the stacking ensemble machine learning method, utilizing four various feature extraction techniques to optimize performance within the stacking ensemble learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we achieved superior results compared to traditional machine learning classifier models. The stacking classifier achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.