# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Fusion of User Comments and News Content
#### 3.5.3 Hierarchical Comment Importance Scoring  
Considering that not all comments are equally informative for credibility assessment, we implement a hierarchical scoring mechanism to evaluate comment importance across multiple dimensions. The relevance score measures semantic similarity between comments and news text:  
\[S_{rel}(c_{i})=\text{cosine\_similarity}(c_{i},H_{T}) \tag{22}\]  
where \(S_{rel}(c_{i})\) represents the relevance score for comment \(c_{i}\), \(c_{i}\) denotes the \(i\)-th comment representation, \(H_{T}\) represents the news text feature representation, and cosine_similarity computes the normalized dot product between comment and text embeddings. The credibility score evaluates trustworthiness indicators within comments:  
\[S_{cred}(c_{i})=\text{CredibilityScorer}(c_{i}) \tag{23}\]where \(S_{crd}(c_{i})\) represents the credibility score for comment \(c_{i}\), \(c_{i}\) denotes the comment content, and CredibilityScorer is a specialized neural network that analyzes linguistic patterns, factual consistency, and other credibility indicators within the comment text. The temporal importance score accounts for comment recency:  
\[S_{temp}(c_{i})=\exp(-\lambda\cdot(t_{current}-t_{ comment})) \tag{24}\]  
where \(S_{temp}(c_{i})\) represents the temporal importance score for comment \(c_{i}\), \(\lambda\) is a decay parameter controlling the rate of temporal degradation, \(t_{current}\) denotes the current timestamp, \(t_{ comment}\) represents the comment posting time, and the exponential function ensures recent comments receive higher weights. The overall importance score combines all three dimensions:  
\[S_{importance}(c_{i})=\alpha\cdot S_{rel}(c_{i})+\beta\cdot S_{crd}(c_{i})+ \gamma\cdot S_{temp}(c_{i}) \tag{25}\]  
where \(S_{importance}(c_{i})\) represents the final importance score for comment \(c_{i}\); \(\alpha\), \(\beta\), and \(\gamma\) are learnable weighting parameters that balance the contribution of relevance, credibility, and temporal factors, respectively; and the linear combination produces a comprehensive measure of comment informativeness for fake news detection. The core idea of Algorithm 2 is to assign an importance weight to each user comment to identify the most valuable comments for fake news detection. The algorithm provides a systematic approach to evaluating comment quality across multiple dimensions. By combining relevance, credibility, and temporal factors, this algorithm automatically identifies the most valuable comments for fake news detection, focusing the model's attention on expert opinions, fact-checking information, and timely responses while filtering out noise and irrelevant content. Relevance Score (\(S_{rel}\)): By calculating the cosine similarity between comment features and news text features, it measures the degree of relevance between comments and main content. Comments with high relevance typically contain direct discussions or questioning of news content. Credibility Score (\(S_{crd}\)): Through a specialized credibility scorer, it identifies credibility indicators in comments such as fact-checking information, source citations, and professional knowledge. These comments often come from users with professional backgrounds or contain verifiable information. Temporal Importance (\(S_{temp}\)): An exponential decay function is used to model the publication time of comments. Early comments usually contain more original information and immediate reactions, making them more valuable for detecting fake news.