A Graph Neural Network Framework for Offensive Language and Hate Speech Identification
A Aishwarya Roy
M.Tech Student, Department of Computer Science and Engineering All Saints College of Technology, Bhopal, India
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) aishwarya.roy2811@gmail.com
B Prof. Sarwesh Site
Associate Professor, Department of Computer Science and Engineering All Saints College of Technology, Bhopal, India
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) er.sarwesh@gmail.com
ABSTRACT
It is critical to have efficient automated detection systems in place since the proliferation of hate speech and inflammatory language on social media platforms has been accelerated by their fast development. Attempts to capture contextual connections were a challenge for traditional machine learning methods like SVM and Logistic Regression, which depended on manually created features. Though they mostly handled text as sequences and neglected underlying relationship structures, deep learning and Transformer-based models learned contextual embeddings, which enhanced performance. To overcome this shortcoming, this research presents a GNN-based framework for modeling textual data as graphs, which allows for a more comprehensive portrayal of the semantic and relational relationships between words and texts. For experimental assessment, three datasets were used: Davidson, which had 24,783 tweets with 5.8% hate, 77.4% offensive, and 16.8% neither; HASOC, which contained around 9,000-12,000 tweets in English and Hindi, with a somewhat balanced distribution; and Founta, which contained 80,000+ tweets in all three categories (hate, abusive, and normal). Baselines such as Logistic Regression, SVM, CNN, BiLSTM, and BERT classifiers were tested against the suggested architecture, which combines GCN, GAT, and GraphSAGE with pre-trained embeddings (GloVe and BERT). Dataset after dataset shows that GNN-based models perform far better than baselines. The F1-scores of GraphSAGE and BERT on the Davidson dataset were 91.7% and 87.8%, respectively. For the HASOC dataset, GraphSAGE achieved an F1-score of 89.9%, which was higher than BERT's 85.5%. Again, GraphSAGE topped the Founta dataset with a 90.2% F1-score, well surpassing BERT's 86.1% performance. Compared to the most advanced Transformer models, these findings show an improvement of 3-5% in F1-score. The results show that abusive language and hate speech identification are better handled by graph-based representations, which give a more complete picture of the linguistic and relational environment. This study lays the groundwork for future research on multimodal, multilingual, and real-time detection systems, and it also sets GNNs as a strong and scalable method for online content moderation.
Keywords: Handwritten Hate Speech, Offensive Language, Graph Neural Networks (GNNs), GCN, GAT, GraphSAGE, Social Media, Text Classification,
