Cyberbullying Recognition on social media for an Afro-Asiatic language Content Using Attention Mechanism and Bi-RNN Classifier
R Rajesh, Muluken Ayele Alemu, Nune Sreenivas, Akey Sungheetha, Sheila Mahapatra · Procedia Computer Science · 2025
In the present era, cyberbullying on social media has developed into a complicated issue. In Ethiopia, cyberbullying involving sexual content material has become a prevalent issue in recent times. Because the content material on social media is unstructured, cyberbullying that is just motivated by sexual texts on the platform can be a laborious and complicated process. The difficulty of finding sexual texts on public media has raised in recent years, leading some experts to focus their attention on the detection of cyberbullying. Because deep learning algorithms do greatly on tasks involving natural language processing, several academics have suggested using these models to detect cyberbullying. Additionally, the majority of research on this challenge count number has focused on socio-political context, handicap, religion, and ethnicity. As a remedy, this observer suggested a thorough investigation of cyberbullying detection for Amharic sexual writing on social media. To create models, a binary Amharic sexual dataset is paired with a sexual dataset trained from collected Amharic content from the Facebook platform. "Bullying" and "Non-bullying" are the binary classes that make up the prepared dataset. Because the W2Vec model works well for describing non-unusual place key phrases in constrained period datasets, it is mostly based on a Skip-gram model. Furthermore, GRU and LSTM networks are used for model comparison, while the Bi-RNN and Attention Mechanism models are employed for classification. Trends in the application of N-fold cross-validation are understood. N-fold cross-validation on our dataset yields very good universal overall performance, according to the results.