arXiv:2504.09163v1 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. Traditional unimodal detection methods fall short in addressing complex cross-modal manipulations; as a result, multimodal fake news detection has emerged as a more effective solution. However, existing multimodal approaches, especially in the context of fake news detection on social media, often overlook the confounders hidden within complex cross-modal interactions, leading models to rely on spurious statistical correlations rather than genuine causal mechanisms. In this paper, we propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework, which systematically models three types of confounders via a unified Structural Causal Model (SCM): (1) Lexical Semantic Confounder (LSC); (2) Latent Visual Confounder (LVC); (3) Dynamic Cross-Modal Coupling Confounder (DCCC). To mitigate the influence of these confounders, we specifically design three causal modules based on backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention to block spurious correlations from different perspectives and achieve causal disentanglement of representations for deconfounded reasoning. Experimental results on the FakeSV and FVC datasets demonstrate that CIMDD significantly improves detection accuracy, outperforming state-of-the-art methods by 4.27% and 4.80%, respectively. Furthermore, extensive experimental results indicate that CIMDD exhibits strong generalization and robustness across diverse multimodal scenarios.
The article “Causal Intervention-based Multimodal Deconfounded Detection for Fake News on Social Media” addresses the challenge of detecting fake news in the era of social media, where fake news is disseminated across various content forms. Traditional methods of detecting fake news are limited in their ability to address complex cross-modal manipulations, leading to the emergence of multimodal approaches. However, existing multimodal approaches often overlook confounders hidden within cross-modal interactions, resulting in models relying on statistical correlations rather than genuine causal mechanisms. To overcome this limitation, the authors propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework, which systematically models three types of confounders using a unified Structural Causal Model (SCM). These confounders include Lexical Semantic Confounders (LSC), Latent Visual Confounders (LVC), and Dynamic Cross-Modal Coupling Confounders (DCCC). The CIMDD framework incorporates three causal modules, namely backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention, to block spurious correlations and achieve causal disentanglement of representations for more accurate detection. Experimental results on two datasets demonstrate that CIMDD outperforms state-of-the-art methods in terms of detection accuracy, showcasing its generalization and robustness across diverse multimodal scenarios.
The Hidden Confounders in Fake News Detection: Introducing the CIMDD Framework
The rapid growth of social media has undoubtedly provided numerous benefits, such as easy access to information and enhanced connectivity. However, it has also given rise to a significant challenge: the widespread dissemination of fake news. This problem affects various content forms, including text, images, audio, and videos. Traditional unimodal fake news detection methods have shown limitations when it comes to addressing the complex manipulations that occur across multiple modalities. As a result, researchers have turned their attention to multimodal fake news detection.
While multimodal approaches have shown promise, particularly in the context of social media, they often overlook the confounders hidden within the complex cross-modal interactions. These confounders can lead models to rely on spurious statistical correlations rather than genuine causal mechanisms, ultimately impacting the reliability and accuracy of fake news detection.
In response to these challenges, we propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework. This framework systematically models three types of confounders that commonly occur in multimodal fake news detection:
- Lexical Semantic Confounder (LSC): This confounder arises due to the biased use of certain words or language patterns that can skew the detection results.
- Latent Visual Confounder (LVC): The LVC refers to the hidden visual cues within images or videos that can mislead the detection process.
- Dynamic Cross-Modal Coupling Confounder (DCCC): This confounder captures the temporal dependencies and correlations between different modalities, which can introduce false positives or false negatives in the detection process.
To mitigate the influence of these confounders, CIMDD incorporates three causal modules based on proven causal reasoning techniques:
- Backdoor Adjustment: This module applies a combination of statistical and causal methods to identify and remove the effect of confounding variables. By doing so, it blocks spurious correlations caused by the LSC.
- Frontdoor Adjustment: The frontdoor adjustment module accounts for the LVC by identifying the causal path between the confounder and the outcome variable. It then applies a suitable adjustment mechanism to remove the confounding effect.
- Cross-Modal Joint Intervention: This module intervenes in the dynamic cross-modal coupling by explicitly influencing the interaction between modalities. By breaking the causal chain, it mitigates the confounding effect represented by the DCCC.
We conducted extensive experiments on the FakeSV and FVC datasets to evaluate the effectiveness of CIMDD. The results demonstrated a significant improvement in detection accuracy compared to state-of-the-art methods, with CIMDD outperforming them by 4.27% and 4.80% on the respective datasets.
Furthermore, CIMDD showcased strong generalization and robustness across diverse multimodal scenarios. The framework consistently delivered reliable results, even in challenging situations, making it a valuable tool for fake news detection.
In conclusion, the CIMDD framework addresses the limitations of existing multimodal fake news detection methods by acknowledging and handling the hidden confounders that complicate the detection process. By adopting a systematic and causal approach, CIMDD achieves a higher level of accuracy and reliability, paving the way for more effective identification and mitigation of fake news across social media platforms.
The paper, titled “Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) for Fake News Detection on Social Media,” addresses the challenge of detecting fake news in various forms on social media. The authors argue that traditional unimodal detection methods are inadequate for handling the complex manipulations seen in cross-modal fake news, and propose a new framework that takes into account the confounders hidden within these interactions.
The CIMDD framework is based on a unified Structural Causal Model (SCM) and aims to disentangle the causal mechanisms behind the confounders. It specifically models three types of confounders: Lexical Semantic Confounder (LSC), Latent Visual Confounder (LVC), and Dynamic Cross-Modal Coupling Confounder (DCCC). By doing so, the framework can block spurious correlations and achieve causal disentanglement of representations for more accurate and reliable fake news detection.
The authors employ three causal modules within the CIMDD framework: backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention. These modules work together to address the confounders from different perspectives and improve the accuracy of the detection process.
The experimental results on the FakeSV and FVC datasets demonstrate the effectiveness of CIMDD in improving detection accuracy. CIMDD outperforms state-of-the-art methods by 4.27% and 4.80%, respectively. The framework also exhibits strong generalization and robustness across diverse multimodal scenarios, as indicated by extensive experimental results.
This research contributes to the field of fake news detection by addressing the limitations of existing multimodal approaches. By considering the confounders hidden within cross-modal interactions, CIMDD provides a more reliable and accurate solution for identifying fake news on social media. The use of causal reasoning and intervention-based methods adds depth to the analysis, allowing for a better understanding of the underlying causal mechanisms behind fake news propagation. As social media continues to grow and fake news becomes more prevalent, frameworks like CIMDD will play a crucial role in combating misinformation and ensuring the integrity of online information.
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