# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Fusion of User Comments and News Content
#### 3.5.2 User Comment and Text Fusion (UCT)  
Since user comments often contain rich textual information that can supplement or refute the main news text, we implement a sophisticated fusion mechanism to capture comment-to-text relationships. The query transformation extracts attention queries from comment features:  
\[Q_{CT}=\text{Linear}_{Q}(F_{\text{C}}) \tag{17}\]  
where \(Q_{CT}\) represents the query matrix derived from comment features, \(F_{\text{C}}\) denotes the comment feature representations, and \(\text{Linear}_{Q}\) is a learnable linear transformation layer that projects comment features into the query space for text attention. The key transformation maps news text features to attention keys:  
\[K_{CT}=\text{Linear}_{K}(H_{T}) \tag{18}\]  
where \(K_{CT}\) is the key matrix generated from textual content, \(H_{T}\) represents the news text feature representations, and \(\text{Linear}_{K}\) is a learnable linear transformation that projects text features into the key space. The value transformation converts text features into attention values:  
\[V_{CT}=\text{Linear}_{V}(H_{T}) \tag{19}\]  
where \(V_{CT}\) denotes the value matrix from textual features, \(H_{T}\) represents the same news text feature representations, and \(\text{Linear}_{V}\) is a learnable linear transformation that projects text features into the value space. The attention weight computation applies scaled dot-product attention:  
\[A_{CT}=\text{softmax}\bigg{(}\frac{Q_{CT}K_{CT}^{T}}{\sqrt{d_{k}}}\bigg{)} \tag{20}\]  
where \(A_{CT}\) represents the normalized attention weights, \(Q_{CT}K_{CT}^{T}\) computes the dot product between comment queries and text keys, \(d_{k}\) is the dimensionality of the key vectors used for scaling, and softmax ensures attention weights sum to one. The final attended feature computation aggregates textual information based on attention weights:  
\[F_{CT}=A_{CT}V_{CT} \tag{21}\]  
where \(F_{CT}\) represents the final comment-informed textual features, \(A_{CT}\) are the attention weights, and \(V_{CT}\) are the value representations that are weighted and aggregated to capture relevant textual information guided by comment content. Algorithm 1 achieves deep fusion of user comments with news content, including two main branches:  
UCI Branch (Comment-Image Fusion): Adopts cross-attention mechanism, using comments as queries and image features as keys and values. This design enables the model to identify specific visual elements discussed or questioned by users in comments, such as inconsistencies in images, traces of photo manipulation, or visual content that does not match textual descriptions. UCT Branch (Comment-Text Fusion): Similarly uses the cross-attention mechanism, but with text features as key-value pairs. This enables the model to capture supplementary, corrective, or refuting information from comments regarding text content. Aggregation Mechanism: Combined with the importance weights obtained from the first algorithm, comment features are weighted and aggregated to ensure that high-quality comments play a greater role in the fusion process. Finally, the aggregated comment representation is combined with original text features through feature concatenation.