# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 2 Literature Review
### In-Domain Fake News Detection
Group-level features are typically derived by aggregating individual-level features, such as the percentage of verified users and the average number of followers within a group [50]. Another important feature in social media is user comments. Several approaches have used temporal linguistic features extracted from sequences of user comments for FND. These methods often rely on fusion techniques that incorporate both news article content and user comments to enhance classification accuracy. In early fusion, features from the text and comments are concatenated in the initial stage, allowing the model to learn joint representations, which can be useful when the modalities are complementary. Late fusion processes the text and comments separately and combines the outputs later, typically by averaging or weighting predictions. This approach ensures that each modality retains its distinctive characteristics. (Early fusion combines data from multiple modalities at an initial stage, allowing the model to capture complex interactions between modalities, such as text and images, from the beginning. However, this approach may lead to overfitting when the interactions between modalities are weak or nonexistent. Late fusion, on the other hand, processes each modality independently and combines their outputs at a later stage, preserving the uniqueness of each modality and reducing the risk of overfitting. However, it may miss valuable cross-modal dependencies by treating the modalities separately for most of the process. Hybrid fusion combines elements of both early and late fusion, capturing some cross-modal interactions early while preserving the distinct information of each modality later on. While powerful, hybrid fusion can increase model complexity and computational demands.) These methods, which fuse text and comments, enhance detection accuracy more compared with relying solely on text. For instance, [PERSON] et al. [51] used recurrent neural networks (RNNs) to analyse sequences of user comments using gated recurrent units, achieving an improvement in accuracy of approximately 15% with gated recurrent units (GRUs) compared with traditional machine learning ML models. Theirs method was further refined by [PERSON] et al. [52], who categorized classified user comments into various categories such as help, reject, question, and comment. They emphasized that the nature of user responses differs depending on the dissemination phase of the news. Similarly, [PERSON] et al. [53] found that fake news tends to elicits more negative responses and questions compared with real news, particularly in the early stages when users have difficulty assessing its credibility. Recent studies have increasingly focused on the sentiment and emotion analysis of comments as a means to enhance the accuracy of fake news detection models. For instance, [PERSON] et al. [28] reported an emotion-based framework that incorporates both publisher details and social emotions that improves the detection accuracy to a notable 87% by considering emotional signals from the content and user comments. In 2023, [PERSON] et al. [27] demonstrated the importance of using sentiment and emotion analysis in FND. Their approach achieved 90% accuracy, although they acknowledged the complexity of accurately capturing and interpreting emotional features from diverse social media data. Despite these advancements, explainability remains challenging. Many studies, such as [PERSON] et al. [54] and [PERSON] et al. [55], offer explanations for their predictions on the basis of particular sentences and user comments. [PERSON] et al. 's model achieved 90% accuracy, and [PERSON] et al. 's method saw a 2% improvement in accuracy over the previous approach [54]. However, the reliance on high-quality labeled datasets for training makes it difficult to generalize these models across different domains. Only a few systems possess robust explainability features, which are critical for gaining trust and ensuring that the models' predictions are transparent to users.