Keywords
Machine Learning, Cyberbullying Detection, Neural Networks, Multiclass Classification, Deep Learning
Description
Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance due to their robustness in capturing temporal dependencies in textual data. This study not only enhances cyberbullying detection methodologies but also contributes to the broader effort of ensuring safer online interactions during public health crises and beyond.
Abstract
Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance due to their robustness in capturing temporal dependencies in textual data. This study not only enhances cyberbullying detection methodologies but also contributes to the broader effort of ensuring safer online interactions during public health crises and beyond.
Course Name
STA 6367 Data Science 2
Instructor Name
Dr rui Xie
College
College of Engineering and Computer Science
STARS Citation
Alinejad, Mahyar, "Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst the COVID-19 Pandemic" (2025). Data Science and Data Mining. 46.
https://stars.library.ucf.edu/data-science-mining/46