arXiv:2406.19776v1 Announce Type: new
Abstract: Fake news detection has received increasing attention from researchers in recent years, especially multi-modal fake news detection containing both text and images.However, many previous works have fed two modal features, text and image, into a binary classifier after a simple concatenation or attention mechanism, in which the features contain a large amount of noise inherent in the data,which in turn leads to intra- and inter-modal uncertainty.In addition, although many methods based on simply splicing two modalities have achieved more prominent results, these methods ignore the drawback of holding fixed weights across modalities, which would lead to some features with higher impact factors being ignored.To alleviate the above problems, we propose a new dynamic fusion framework dubbed MDF for fake news detection.As far as we know, it is the first attempt of dynamic fusion framework in the field of fake news detection.Specifically, our model consists of two main components:(1) UEM as an uncertainty modeling module employing a multi-head attention mechanism to model intra-modal uncertainty; and (2) DFN is a dynamic fusion module based on D-S evidence theory for dynamically fusing the weights of two modalities, text and image.In order to present better results for the dynamic fusion framework, we use GAT for inter-modal uncertainty and weight modeling before DFN.Extensive experiments on two benchmark datasets demonstrate the effectiveness and superior performance of the MDF framework.We also conducted a systematic ablation study to gain insight into our motivation and architectural design.We make our model publicly available to:https://github.com/CoisiniStar/MDF
Fake News Detection and the Multi-disciplinary Nature of Multimedia Information Systems
Fake news detection has become an increasingly important area of research in recent years, as the impact and spread of misinformation continues to grow. In particular, the detection of multi-modal fake news, which combines both text and images, poses a significant challenge due to the inherent noise present in the data.
Previous works have attempted to address this challenge by simply concatenating or applying attention mechanisms to the text and image features before feeding them into a binary classifier. However, this approach often leads to intra- and inter-modal uncertainty, as the noise in the features is not properly accounted for. Additionally, the fixed weights across modalities used in many methods ignore the potential impact of certain features, which can limit the accuracy of the detection.
In response to these limitations, the authors propose a new dynamic fusion framework called MDF for fake news detection. This framework consists of two main components: an uncertainty modeling module called UEM, which uses a multi-head attention mechanism to model intra-modal uncertainty, and a dynamic fusion module called DFN, which utilizes D-S evidence theory to dynamically fuse the weights of the text and image modalities.
To further improve the performance of the dynamic fusion framework, the authors incorporate the Graph Attention Network (GAT) for inter-modal uncertainty and weight modeling before the DFN stage. This multi-disciplinary approach, combining techniques from deep learning (attention mechanisms, GAT), uncertainty modeling, and evidence theory, allows for a more comprehensive and robust detection of fake news.
The proposed MDF framework was evaluated on two benchmark datasets, and the results demonstrate its effectiveness and superior performance compared to previous methods. Additionally, a systematic ablation study was conducted to gain insight into the motivation and design of the framework, further reinforcing its potential applicability in real-world scenarios.
The concepts and methodologies presented in this article have direct implications for the wider field of multimedia information systems. Multimedia information systems deal with the processing, organization, and retrieval of multimedia data, which includes text, images, audio, and video. Fake news detection, as a specific application of multimedia information systems, demonstrates the importance of considering multiple modalities and the challenges in dealing with noisy and uncertain data.
Furthermore, the MDF framework and its incorporation of techniques such as attention mechanisms, GAT, and uncertainty modeling align with the advancements in technologies like animations, artificial reality, augmented reality, and virtual realities. These technologies often rely on a fusion of different modalities, such as combining virtual objects with real-world images or integrating virtual elements into physical environments. The MDF framework’s dynamic fusion approach can potentially contribute to the development of more robust and immersive multimedia experiences in these domains.
In conclusion, the proposed MDF framework represents a novel and multi-disciplinary approach to fake news detection, addressing the challenges of noisy and uncertain multi-modal data. Its integration of uncertainty modeling, evidence theory, and advanced deep learning techniques showcases the potential of applying multimedia information systems concepts to real-world problems. As the field of multimedia information systems continues to evolve, the lessons learned from fake news detection can contribute to the advancement of technologies such as animations, artificial reality, augmented reality, and virtual realities.