# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 2 Literature Review
### Cross-Domain Fake News Detection  
Cross-domain fake news detection aims to develop models capable of predicting and identifying future instances of fake news that may not belong to the same domains as those used during the model's training phase. The primary goal of such models is to anticipate and adapt to previously unseen types of fake news, making them highly versatile and robust. However, this area of research encounters significant challenges due to its complexity and the dynamic nature of fake news, which continually evolves across different domains. These challenges include:  
1. Lack of diverse data: finding comprehensive datasets that span multiple domains represents a substantial challenge. The diversity and breadth of such data are crucial for building models that can accurately detect fake news across a wide range of topics and formats. Without extensive and varied datasets, models may struggle to generalize well beyond their training environments. 2. Domain-specific features: adapting features that are specific to one domain for use in another is inherently difficult. Features that are highly indicative of fake news in one context may not be relevant or could even be misleading in another. This necessitates the development of sophisticated algorithms capable of identifying and leveraging transferable features that maintain their significance across various domains. 3. Limited transfer learning techniques: although transfer learning offers a promising approach to cross-domain fake news detection, existing methods are still under refinement. Developing techniques that can seamlessly transfer knowledge from one domain to another without significant loss of accuracy or relevance remains a key research focus. Enhancements in this area are critical for creating models that can adapt to new and emerging forms of misinformation with minimal need for retraining. In the literature, cross-domain fake news detection primarily concentrates on the structure of the news. The underlying concept is that, regardless of the domain, all news shares a common structure, which can include aspects such as the number of parts of speech, punctuation symbols, and others. [PERSON] et al. [22] were among the pioneers in analyzing fake news detection (FND) using a cross-domain approach. They demonstrated that their proposed method performed well within the same domain, achieving an accuracy of 74%, but its effectiveness significantly decreased to 56% in cross-domain analysis. Additionally, they introduced two datasets: FakeNewsAMT and Celebrity. Building on this foundation, in 2020, [PERSON] et al. [23] proposed another cross-domain analysis method utilizing tools such as Spinbot (for paraphrasing), Grammar (for grammar checking), and GloVe (for word embedding). While their method achieved a 95% accuracy on the FakeNewsAMT dataset, the accuracy dropped to 70% when the model was trained on the Celebrity dataset. This highlighted the ongoing challenge of maintaining high performance across different domains. [PERSON] et al. [24] further explored this issue by introducing an ELMo-based word embedding approach. Their method showed an in-domain accuracy of 83.3% but a reduced cross-domain accuracy of 68.5%, indicating a decline from previous methods [22; 23]. This trend underscores the difficulty of achieving robust cross-domain performance in fake news detection. Continuing this line of research, [PERSON] et al. [25] experimented with RoBERT, a sentence embedding-based transformer model. Although it exhibited excellent in-domain performance with 99% accuracy on the FakeNewsAMT dataset, it faced significant performance degradation in the cross-domain scenario, with accuracy dropping to 59% when trained on the Celebrity dataset. Finally, [PERSON] et al. [56] used structured features, including parts of speech with word-based features, and achieved 70% accuracy when tested with the Celebrity dataset. Though the overall accuracy increased, there was a significant loss in accuracy for the fake class.