  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.
