# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 6 Conclusions
### Limitations and Future Directions  
While UC-CMAF demonstrates strong performance across multiple datasets, several limitations warrant discussion for future research directions. Both ReCOVery and MMCoVaR are centered around COVID-19-related misinformation, which may introduce topic-specific linguistic, visual, and social biases. For instance, common text tokens (e.g., \"vaccine,\" \"clinical trial\") and visual patterns (e.g., medical diagrams or bar charts) may dominate feature learning, while user comments tend to reflect highly polarized or emotionally charged discourse. As a result, the model may learn domain-specific representations that do not generalize well to topics such as politics, finance, or AI-generated content. However, the modular design of UC-CMAF--including separate CMA, UCI, and UCT components--allows for efficient adaptation to new domains via fine-tuning without altering the core architecture. Future work should evaluate the framework on a broader range of datasets, such as PolitiFact or GossipCop, to systematically assess its robustness across diverse semantic and visual distributions. Comment Availability Dependency: Our framework's effectiveness is fundamentally tied to the presence and quality of user comments. In scenarios where comments are sparse--such as newly published articles, niche topics, or platforms with limited user engagement--the model's performance may degrade as it relies more heavily on traditional text-image analysis. Future work should explore hybrid approaches that can gracefully handle comment-scarce environments. Cross-linguistic and Cross-platform Adaptability: The current evaluation focuses primarily on English-language content from specific social media platforms. Extending to multilingual scenarios presents challenges, including (1) linguistic variations in commenting behavior across cultures, (2) different misinformation propagation patterns in various linguistic communities, and (3) platform-specific user interaction mechanisms. Future research should investigate transfer learning approaches and culturally aware adaptation strategies. Temporal Dynamics: Our model treats comment collections as relatively static, but real-world comment threads evolve continuously. The temporal importance scoring mechanism may need enhancement to better capture the dynamic nature of evolving misinformation narratives and user response patterns. Computational Scalability: As demonstrated in our analysis, processing large numbers of comments significantly increases computational overhead. While our importance scoring helps mitigate this issue, real-time deployment scenarios may require further optimization strategies or adaptive sampling techniques. Adversarial Robustness: Although our framework shows resistance to various attack types, sophisticated adversaries might develop coordinated comment manipulation strategies specifically targeting our importance scoring mechanism, necessitating ongoing defensive improvements. Multimodal Perception and Social Signal Integration: Although UC-CMAF is evaluated empirically, its design is inspired by cognitive principles of multimodal perception and social reasoning. Human perception tends to integrate semantically consistent signals across modalities while filtering out irrelevant inputs--an idea reflected in our bidirectional adaptive attention mechanism, which selectively enhances aligned text-image regions and dynamically adjusts fusion weights based on contextual relevance. In addition, our use of user comments draws on the notion that social discourse conveys distributed credibility signals; comments often express skepticism, support, or counter-evidence that complement or challenge the source content. By leveraging these social cues through dedicated attention pathways, UC-CMAF models a form of collective reasoning, enhancing its effectiveness in detecting multimodal misinformation.