Kaliyar, Rohit Kumar and Dash, Pallavi and Jyothi, Lakshaga M. (2021) RUEvaL20: Improving Rumour Detection on Social Media using a Deep Convolutional Neural Network. CODS-COMAD 2021: PROCEEDINGS OF THE 3RD ACM INDIA JOINT INTERNATIONAL CONFERENCE ON DATA SCIENCE & MANAGEMENT OF DATA (8TH ACM IKDD CODS & 26TH COMAD). p. 439.
Full text not available from this repository.Abstract
Due to the tremendous increase in the activity of social media, there has been a considerable drift in the propagation of rumours causing damage to several organizations or personal. Since last few years, a significant exploration has been done due to hazardous effects of rumours on society. In this paper, we propose a deep neural network (RUEvaL20) for effective rumour detection. Experiments have been conducted with real-world dataset: PHEME. We have achieved remarkable results, and our proposed model outperformed with most of the existing state-of-the-art methods.
| Item Type: | Article |
|---|---|
| Subjects: | Computer Science > Artificial Intelligence Computer Science > Computer Science Computer Science > Information Systems |
| Depositing User: | Unnamed user with email techsupport@mosys.org |
| Last Modified: | 06 Feb 2026 07:10 |
| URI: | https://ir.vmrfdu.edu.in/id/eprint/6715 |
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