# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 2 Literature Review
### In-Domain Fake News Detection  
Research on fake news detection primarily focuses on in-domain literature. In the context of this research, 'in-domain' refers to situations where both the training and test datasets belong to the same domain. Some examples of domain include war, COVID-19, vaccines, etc. Research on fake news detection primarily focuses on in-domain literature, indicating a significant emphasis on methodologies and technologies developed and evaluated within the sphere of specific content domains. In the context of this research, 'in-domain' is a term that specifically refers to situations where both the training and testing datasets belong to the same domain or subject area. This approach ensures that the models developed for fake news detection are finely tuned to the nuances and characteristics typical of the content within that domain. The rationale behind this focus stems from the understanding that fake news often contains subtle cues and context-specific indicators that are best identified when models are trained and tested on data from the same domain. This enhances the accuracy of detection algorithms, enabling them to effectively discern between true and fake news within tightly defined content boundaries. * Content-based FND: content-based methods (news text) are the most commonly used techniques for fake news detection in in-domain scenarios. These methods are currently focused on utilizing transformer-based models for feature extraction and classification. [PERSON] et al. [36] used over 8 million tweets about the U.S. general election to develop a bidirectional training approach. This method improves fake news classification by capturing semantic and long-distance dependencies, achieving a 98.90% accuracy with a BERT-based model. [PERSON] et al. [37] fine-tuned BERT for detecting fake news in a Korean dataset, achieving an ROC-AUC score of 83.8%. [PERSON] et al. [38] proposed a BERT-CNN model, which outperformed five state-of-the-art models in F1-score on Arabic, Greek, and Turkish tweets, suggesting potential improvements for other languages. In addition to transformer-based methods, TF-IDF, part-of-speech tagging, and word embeddings are also common in content-based fake news detection [39; 40; 41]. Since BERT is a highly powerful semantic feature extractor, it is widely used in numerous studies [42; 43]. However, its computational intensity can be a limiting factor in some applications. In addition to BERT-based models, other transformers like RoBERT, XBert, and GPT perform well for fake news detection [44; 45]. [PERSON] et al. [46] introduced a single-layer CNN model integrated with BERT, evaluated on the Airline Travel Information Systems (ATIS) dataset, achieving 98.54% accuracy. They noted the model's suitability for short sentences and potential limitations in robustness. Other attention- and sentiment-based methods have also been studied in the literature [30; 47; 48]. However, these methods may struggle with complex language and context variability. * Social media- based FND: detecting fake news in traditional news media primarily relies on the content of the news itself. However, in social media, additional contextual information such as user profiles, comments, and news propagation patterns can assist in identifying fake news. The role of users is crucial in this context, as both humans and bots can disseminate news. Users provide valuable information for fake news detection, and user-based features reflect the characteristics of those interacting with news on social media. These features are categorized into individual and group levels. Individual-level features assess each user's credibility and reliability by examining demographics such as account age, number of followers/following, and the volume of tweets authored [49]. On the group level, it is assumed that the communities spreading fake news differ from those spreading real news.