Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques

Electronics

Ayush Agarwal¹  Bhuva Narayan²  Chahat Raj³  Gnana Bharathy³  Mukesh Prasad³ 

[1] Department of Information Technology, Delhi Technological University, Delhi 110042, India;School of Communication, FASS, University of Technology Sydney, Sydney, NSW 2007, Australia;School of Computer Science, FEIT, University of Technology Sydney, Sydney, NSW 2007, Australia;

关键词: cyberbullying;    hate speech;    offensive language;    machine learning;    neural networks;    deep learning;   

DOI  :  10.3390/electronics10222810

来源: DOAJ

【 摘 要 】

The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.

【 授权许可】

Unknown