arXiv:2504.09154v1 Announce Type: new
Abstract: The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Compared to unimodal fake news detection, multimodal fake news detection benefits from the increased availability of information across multiple modalities. However, in the context of social media, certain modalities in multimodal fake news detection tasks may contain disruptive or over-expressive information. These elements often include exaggerated or embellished content. We define this phenomenon as modality disruption and explore its impact on detection models through experiments. To address the issue of modality disruption in a targeted manner, we propose a multimodal fake news detection framework, FND-MoE. Additionally, we design a two-pass feature selection mechanism to further mitigate the impact of modality disruption. Extensive experiments on the FakeSV and FVC-2018 datasets demonstrate that FND-MoE significantly outperforms state-of-the-art methods, with accuracy improvements of 3.45% and 3.71% on the respective datasets compared to baseline models.
Expert Commentary: The Multi-Disciplinary Nature of Fake News Detection in Multimedia Information Systems
Fake news has become a major concern in today’s digital age, and its dissemination across various forms of media can have wide-ranging consequences. This study highlights the importance of multimodal fake news detection, where information from multiple modalities, such as text, images, audio, and video, is used to identify and classify fake news. By leveraging the availability of diverse information sources, multimodal detection has the potential to offer improved accuracy and robustness compared to unimodal approaches.
One significant challenge in multimodal fake news detection is the presence of disruptive or over-expressive content within certain modalities. This disruptive information can be characterized by exaggerated or embellished elements that are often found in social media posts. The authors of this study refer to this phenomenon as “modality disruption,” and they explore its impact on detection models through a series of experiments.
To address the issue of modality disruption, the researchers propose a multimodal fake news detection framework called FND-MoE. This framework aims to target and mitigate the disruptive effects of modality by incorporating a two-pass feature selection mechanism. By selecting relevant and reliable features from the various modalities, FND-MoE seeks to minimize the influence of exaggerated or embellished content on the overall detection performance.
The experiments conducted on the FakeSV and FVC-2018 datasets demonstrate the effectiveness of FND-MoE in mitigating the impact of modality disruption. The framework outperforms state-of-the-art methods and shows significant accuracy improvements of 3.45% and 3.71% on the respective datasets compared to baseline models. These results indicate the potential practical applicability of FND-MoE in real-world scenarios where fake news detection is crucial.
Connections to Multimedia Information Systems and Related Fields
The concept of multimodal fake news detection discussed in this article highlights the multi-disciplinary nature of the field. It brings together concepts and techniques from various disciplines such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
In the context of multimedia information systems, this study illustrates how different media modalities can be combined to enhance the accuracy and effectiveness of fake news detection. By leveraging information from text, images, audio, and video, researchers can develop more comprehensive and robust detection models. This integration of multiple modalities is a vital aspect of multimedia information systems, which aim to analyze and process diverse forms of media for various purposes.
The relevance of animations, artificial reality, augmented reality, and virtual realities lies in the fact that fake news can also be disseminated and propagated through these mediums. By taking into account the distinct characteristics and challenges posed by these immersive and interactive technologies, researchers can develop more specialized detection techniques. For instance, the detection of fake news in virtual reality environments requires an understanding of the unique features and manipulations that can occur within these simulated worlds.
In conclusion, multimodal fake news detection is an interdisciplinary field that draws upon concepts and methodologies from various disciplines, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The proposed FND-MoE framework showcases the potential benefits of integrating multiple modalities in combating fake news while addressing the challenges posed by disruptive or over-expressive content within certain modalities.