Abstract:
With the burgeoning use of social media platforms,
online harassment and cyberbullying have become significant
concerns. Traditional mechanisms often falter, necessitating
advanced methodologies for efficient detection. This study
presents an innovative approach to identifying cyberbullying
incidents on social media sites, employing a hybrid neural network
architecture that amalgamates Long Short-Term Memory
(LSTM) and Convolutional Neural Network (CNN). By
harnessing the sequential processing capabilities of LSTM to
analyze the temporal progression of textual data, and the spatial
discernment of CNN to pinpoint bullying keywords and patterns,
the model demonstrates substantial improvement in detection
accuracy compared to extant methods. A diverse dataset,
encompassing multiple social media platforms and linguistic
styles, was utilized to train and test the model, ensuring
robustness. Results evince that the LSTM-CNN amalgamation can
adeptly handle varied sentence structures and contextual nuances,
outstripping traditional machine learning classifiers in both
specificity and sensitivity. This research underscores the potential
of hybrid neural networks in addressing contemporary digital
challenges, urging further exploration into blended architectures
for nuanced problem-solving in cyber realms.
