Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection
This work addresses cyberbullying detection for social media platforms, but it is incremental as it builds on existing methods with new linguistic embeddings and feature density concepts.
The study tackled improving cyberbullying detection by applying linguistic preprocessing and feature density analysis to machine learning classifiers, finding that neural networks are effective and feature density correlates with performance, with specific gains like improved accuracy on a re-annotated Formspring dataset.
In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were used, with the specific focus on linguistically-backed embeddings in Convolutional Neural Networks (CNN). Moreover, we study the concept of Feature Density and confirm its potential to comparatively predict the performance of ML classifiers, including CNN. The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The dataset was re-annotated by objective experts (psychologists), as the importance of professional annotation in cyberbullying research has been indicated multiple times. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density while also proposing a new approach of training various linguistically-backed embeddings for Convolutional Neural Networks.