MTBullyGNN
A Graph Neural Network-Based Multitask Framework for Cyberbullying Detection
Bibliographic Data
| ID | 22106976 |
|---|---|
| Authors | Krishanu Maity (0000-0002-9542-9250, Indian Institute of Technology Patna), Tanmay Sen (0000-0003-3642-4131, Ericsson, Kolkata, India), Sriparna Saha (0000-0001-5458-9381, Indian Institute of Technology Patna), Pushpak Bhattacharyya (0000-0001-5319-5508, Indian Institute of Technology Bombay) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Pages | 849-858 |
| Publication date | 2024-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3230974 |
| OpenAlex | W4313270711 |
| Language | EN |
| Citations received | 2 |
| References cited | 31 |
Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-aided cyberbullying detection (CD) from code-mixed language. The GNN helps detect unlabelled or noisy label nodes (sentences) accurately by aggregating information from similarly labeled nodes. To connect nodes, we apply cosine similarity between sentences and create a single text graph for a benchmark code-mixed cyberbullying corpus, BullySent. Experimental results illustrate that MTBullyGNN outperforms the state-of-the-art (SOTA) methods for both the single (CD) and MT (CD and sentiment) settings by up to 4.46% and 4.92% in classification accuracy, respectively. Furthermore, another benchmark Hindi–English code-mixed single-task dataset has also been considered to illustrate the robustness of our proposed model. The code will be made publicly available in the camera-ready version
Artificial neural network · Graph · Machine learning · Multi-task learning · Natural language processing · Programming language · Source code · Bullying, Victimization, and Aggression · Computer Science · Hate Speech and Cyberbullying Detection · Artificial Intelligence · Theoretical Computer Science
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |