## 2 Overview of Fake News and Fake Profiles
### Fake Profile Detection
#### 2.3.1 Profile Attribute Analysis  
Profile attribute analysis is crucial for distinguishing between genuine and fake social media accounts. This analysis employs two main approaches: _Profile Picture Verification_ and _Consistency of Profile Information_. Each method involves various ML techniques and algorithms to ensure the authenticity of profiles, thereby enhancing the security and reliability of social networking platforms.  
The _Profile Picture Verification_ approach leverages several methods to verify the authenticity of profile pictures. One such method is the Photo-Response Non-Uniformity (PRNU) technique, which can be successfully used as a watermarking approach for authorship attribution and verification of pictures on social networks. According to [39], PRNU employs a unique noise pattern for each camera that withstands the compression algorithms applied by various social platforms, unlike traditional watermarking techniques. This method enables accurate detection of fake accounts without altering the original image's appearance.  
Additionally, a new algorithm named SVM-NN is proposed by [36] to provide efficient detection for fake Twitter accounts and bots. This machine learning classification algorithm utilizes fewer features than traditional support vector machine (SVM) or neural network (NN) algorithms. The SVM-NN model involves training an NN model using SVM decision values, effectively distinguishing between Sybil and real accounts.  
Moreover, a hybrid model described in [40] uses machine learning and skin detection algorithms to identify fake accounts. This model integrates five supervised machine learning algorithms--K-nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest--to analyze user account features. It achieves up to 80% accuracy in detecting fake accounts by calculating the percentage of skin exposure in profile images, finding that fake accounts often exhibit higher skin percentages.  
The _Consistency of Profile Information_ approach focuses on the textual and metadata consistency of profile information. One significant method is the detection of fake LinkedIn profiles immediately after creation, as described by [41]. Utilizing textual data provided during the registration process, the system employs section and subsection tags (SSTE) to detect fake accounts before they establish connections with real users.  
Furthermore, a data mining approach for fake profile identification, as detailed by [37], includes four well-known techniques: Neural Network (NN), Support Vector Machine (SVM), Principal Component Analysis (PCA), and Weighted Average (WA). PCA is used for dimensionality reduction, selecting the most explanatory profile features. NN, SVM, and WA predict the authenticity of profiles, with SVM using a Polynomial Kernel achieving the highest accuracy at 87.34%.  
Finally, a proposed framework combines the Random Forest Classifier model with pipelining to identify fake profiles across social communities, as described in [42]. The classifier, optimized through grid search, evaluates features like profile picture, bio length, external URL, privacy status, number of posts, followers, and follows. The pipelining process enhances model accuracy by automating the workflow and evaluating the model's performance using a confusion matrix for precise and reliable results.