On the Integration of Social Context for Enhanced Fake News Detection Using Multimodal Fusion Attention Mechanism

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 78 - 78

Published: April 11, 2025

Detecting fake news has become a critical challenge in today’s information-dense society. Existing research on detection predominantly emphasizes multi-modal approaches, focusing primarily textual and visual features. However, despite its clear importance, the integration of social context received limited attention literature. To address this gap, study proposes novel three-dimensional multimodal fusion framework that integrates textual, visual, features for effective media platforms. The proposed methodology leverages an advanced Vision-and-Language Bidirectional Encoder Representations from Transformers multi-task model to extract fused text images concurrently, capturing intricate inter-modal correlations. Comprehensive experiments validate efficacy approach. results demonstrate solution achieves highest balanced accuracy 77%, surpassing other baseline models. Furthermore, incorporation significantly enhances performance. architecture also outperforms state-of-the-art providing robust scalable using artificial intelligence. This contributes advancing field by offering comprehensive practical engineering combating news.

Language: Английский

A feature selection-aided deep learning based deepfake video detection method

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: April 30, 2025

Language: Английский