## 2 Overview of Fake News and Fake Profiles
### Fake Profile Detection
#### 2.3.2 Network Analysis  
In network analysis, various methods are utilized to detect and analyze fake accounts on social media platforms. These methods focus on examining relationships and communities within the network, leveraging graph-based algorithms and community detection techniques to ensure the authenticity of user profiles. _Relationship Graphs_ is a fundamental aspect of detecting fake users. An enhanced graph-based semi-supervised learning algorithm (EGSLA) has been introduced to detect fake users on Twitter by examining user activity over a prolonged period [43]. EGSLA employs dynamic feature changes, which are challenging for spammers to manipulate over time since spammers typically mimic real users temporarily due to the associated costs. By representing user activity through a graph model, EGSLA classifies, predicts, and makes recommendations using scalable streaming for real-time data processing. The algorithm's performance, tested against existing game theory, k-nearest neighbor (KNN), support vector machine (SVM), and decision tree techniques, achieves a 90.3% accuracy in identifying fake users, outperforming other methods and underscoring its effectiveness. Another approach proposes a hybrid method for detecting malicious profiles on Twitter by utilizing machine learning techniques. Initially, a Petri net structure analyzes user profiles and features [44], which are subsequently used to train various classifiers to distinguish between malicious and genuine users. The framework processes baseline data through different classifiers in machine learning environments, creating separate datasets for authentic and fraudulent accounts. By applying reachability checks, the system identifies a harmful activity, involving interaction anomaly detection and addressing low accuracy issues through a trackback mechanism. A comparative analysis demonstrates the framework's efficiency, showing improved performance over state-of-the-art techniques. Furthermore, a real-time Sybil detector for the Renren online social network has been developed, operating based on a graph where nodes represent user profiles and edges denote connections between profiles through interactions and relationships [45], taking into account the interaction anomalies that may exist. The detector monitors account features such as friend-request frequency, outgoing request acceptance rates, and clustering coefficients to identify suspicious behavior. By analyzing the graph structure, particularly patterns of link creation and clustering coefficients, it distinguishes between genuine and Sybil accounts. If an account exceeds predefined limits, it is flagged as a potential Sybil by a threshold-based system. An adaptive feedback scheme dynamically tunes the threshold parameters, leading to the identification and banning of over 100,000 Sybil accounts. _Community Detection_ is another critical method for identifying fake accounts. The Communal Influence Propagation Framework (CIPF) identifies fake accounts by analyzing essential features from individual user profiles, linguistic characteristics, and group profiles (network features) [46]. The framework scrutinizes these features to generate a feature vector of the user space, utilizing Social Media Mining (SMM) theories such as Influence, Homophily, and Balance to enrich the malicious user space as an influential index. The Jaccard coefficient evaluates similarity indices, detects Sockpuppet nodes, and generates a negative propagation belonging matrix. CIPF integrates two influence-based verification models--the Sockpuppet Detection Phase (IB-SPD) and the Convolutional Neural Network-based Crowdturfing Community (IB-CFC)--to robustly identify and verify fake accounts. Additionally, a majority voting approach leverages machine learning algorithms to identify and categorize counterfeit social media profiles [47].