## 2 Overview of Fake News and Fake Profiles
### Detection Methods
#### 2.2.1 Rule-Based Approaches  
Rule-based approaches for fake news detection generally fall into two main methods: _Keyword Analysis_ and _Heuristic Rules_.  
The _Keyword Analysis_ method explores the Positive and Unlabeled Learning (PUL) combined with a network-based Label Propagation (PU-LP) algorithm for detecting fake news [23]. This combination reduces labeling costs while improving the classification task by exploiting relationships between news and terms using a few labeled fake news examples. The PU-LP algorithm is integrated with a Graph Attention Neural Event Embedding (GNEE) to further refine the relevance of terms used in fake news detection. Experiments show that such approaches, especially when using specialized keyword extraction tools, achieve high accuracy with minimal labeled data. The potential incorporation of attention mechanisms is also expected to improve effectiveness in fake news detection.  
Other studies in this group [3; 24] introduce hybrid deep learning models that combine Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for fake news classification. The models leverage CNN's ability to extract local features and the Long Short-Term Memory (LSTM) network's capability to learn long-term dependencies. By processing input data to capture both local and sequential features, the models classify text as fake or not. Validation on popular fake news datasets (ISO and FA-KES) shows that hybrid models significantly outperformed other non-hybrid baseline methods.  
Furthermore, the Interpretable Knowledge-Aware (IKA) model uses historical evidence in the form of graphs for fake news detection [25]. It constructs positive and negative evidence graphs by collecting signals from historical news. IKA compares the given piece to be detected with these evidence graphs to generate a matching vector, enhancing prediction accuracy. Semantic, stylistic, and emotional elements are extracted from news articles and comments and combined with graph-based data for prediction. The model was tested on English and Chinese datasets, achieving high accuracy. IKA is versatile and can be integrated with other existing models to improve their interpretability.  
The _Heuristic Rules_ approach utilizes artificial intelligence algorithms like the Naive Bayes classifier to detect fake news on social media [26]. Each user post represents a news item, with each word processed independently to calculate the probability of the post being fake. The classification process evaluates the likelihood that the post is fraudulent based on the presence of certain words. To ensure numerical stability, the system uses default probabilities for unknown words and logarithmic computations. This method achieves a classification accuracy of approximately 74%, which is acceptable for the simplicity of the approach.  
More advanced deep learning techniques exploit the power of Graph Convolution Neural Networks (GCNs) to analyze the text of user posts and/or user profiles and detect fake news or fake profiles [5]. They also take advantage of the news diffusion paths to further detect hidden attempts to disseminate fake content, by engaging fake and legitimate user accounts [27].