Year: 2024
Type: conference-paper
Abstract: Cyberbullying has become more prevalent over time, especially towards minority groups, and online human moderators cannot detect cyberbullying content efficiently.Prior work has addressed this problem by detecting cyberbullying with deep learning approaches.In this project, we compare several BERT-based benchmark methods for cyberbullying detection and do a failure analysis to see where the model fails to correctly identify cyberbullying.We find that many falsely classified texts are sarcastic, so we propose a method to mitigate the false classifications by incorporating neural network-based sarcasm detection.We define a simple multilayer perceptron (MLP) that incorporates sarcasm detection in the final cyberbully classifications and demonstrate improvement over benchmark methods.
Authors: Xiaoyu Guo, Susan Gauch
Institution: University of Arkansas at Fayetteville
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W7131780400