arXiv:2409.00022v1 Announce Type: new
Abstract: The landscape of social media content has evolved significantly, extending from text to multimodal formats. This evolution presents a significant challenge in combating misinformation. Previous research has primarily focused on single modalities or text-image combinations, leaving a gap in detecting multimodal misinformation. While the concept of entity consistency holds promise in detecting multimodal misinformation, simplifying the representation to a scalar value overlooks the inherent complexities of high-dimensional representations across different modalities. To address these limitations, we propose a Multimedia Misinformation Detection (MultiMD) framework for detecting misinformation from video content by leveraging cross-modal entity consistency. The proposed dual learning approach allows for not only enhancing misinformation detection performance but also improving representation learning of entity consistency across different modalities. Our results demonstrate that MultiMD outperforms state-of-the-art baseline models and underscore the importance of each modality in misinformation detection. Our research provides novel methodological and technical insights into multimodal misinformation detection.
Expert Commentary:
This article explores the challenge of combating misinformation in the evolving landscape of social media content, which has extended from text to multimodal formats. While previous research has primarily focused on single modalities or text-image combinations, there is a gap in detecting multimodal misinformation. This is where the proposed Multimedia Misinformation Detection (MultiMD) framework comes into play.
The MultiMD framework aims to address the limitations of existing methods by leveraging cross-modal entity consistency in video content to detect misinformation. The framework takes a dual learning approach, which not only enhances misinformation detection performance but also improves representation learning of entity consistency across different modalities.
One of the key aspects of this framework is its multi-disciplinary nature. It combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By leveraging the inherent complexities of high-dimensional representations across different modalities, MultiMD is able to provide more accurate and robust detection of multimodal misinformation.
The results of the study demonstrate the effectiveness of the MultiMD framework, as it outperforms state-of-the-art baseline models in detecting misinformation. This reinforces the importance of considering each modality when detecting and combating misinformation in multimedia content.
In the wider field of multimedia information systems, this research contributes novel methodological and technical insights into multimodal misinformation detection. It highlights the need for more comprehensive approaches that take into account the diverse range of content formats present in social media platforms.
Overall, the MultiMD framework has the potential to significantly advance the field of misinformation detection by providing a more holistic and accurate approach to combatting multimodal misinformation. As the landscape of social media content continues to evolve, it is crucial to develop robust techniques that can effectively detect and mitigate the spread of misinformation in various modalities.