# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 3 Related Works  
In recent years, fake accounts have become a serious problem in OSNs. Currently, many researchers have conducted studies on detecting fake accounts on different social platforms. A study in [26] suggested using a trained model of a deep neural network along with specific parameters to identify automatic spam and fake Instagram account profiles. The model for identifying fake Instagram profiles was prepared using the gradient descent technique. The gradient descent was also used to obtain the optimized network loss and bias values. The cost function was minimized iteratively. The proposed method's precision and accuracy were 93% and 91%, respectively. The emphasis on precision over recall reflects their strategy to minimize false positives while detecting fake Instagram accounts. The researchers in [28] proposed a framework for detecting fake profiles on Facebook. This framework would be based on a deep neural network (DNN) classifier. The results showed good efficiency and performance, with an accuracy of 99.4%. This study [29] developed a detection model that uses a range of machine learning approaches to discriminate between real and fake X profiles. Based on features including follower and friend count and status changes, it distinguished between real and fake accounts using the X profile dataset. Moreover, it made use of neural networks, XG Boost, Random Forest, and LSTM. The features are selected to evaluate the veracity of a social media profile; the result of this research showed that XG Boost has the best performance out of the machine learning techniques for finding fake profiles, with 99.6% accuracy. The study in [30] covers a variety of machine learning algorithms, including ensemble approaches (such as Random Forest and XGBoost), support vector machines (SVM), decision trees, Bagging, and Boosting. This research seeks to identify a method for distinguishing between real and fake Instagram profiles based on publicly available profile data. The Synthetic Minority Oversampling Technique (SMOTE) technique was used to make the two data classes equal. This makes it easier to obtain the same number of instances for each class. The results demonstrate an overall highest accuracy of 96% for Random Forest and XGBoost. In the study of [31], a hybrid method based on SMOTE incorporating Differential Evolution (DE) was proposed to facilitate real-time spam rate detection in X datasets. Thestudy was mainly aimed to help in identifying real owners and to filter the spam posts. Incorporating SMOTE would help tackle the imbalance of class distribution in the datasets, while the DE will help tackle the RF hyperparameters. The results of the hybrid method increased accuracy from 89.4% to 99.3%. Another study in [32] proposed a fake account detection framework for effectively classifying an account as true or fake on X. This framework would be based on a logistic regression (LR) classifier. The results showed good efficiency and performance, with an accuracy of 96.2%. A study conducted in [33] proposed incorporating a technique involving AI technology and Natural Language Processing and a way to increase the accuracy level in fake account detection. The two techniques worked by incorporating a RF Classifier, optimized Naive Bayes (NB) technique that help to profiles category, and SVM. The three algorithms' techniques were incorporated to determine the false and right identities of the accounts. These algorithms use fewer features and have a very high accuracy rate of successfully detecting fake profiles. The study conducted in [34] proposed a supervised ML model that would help to classify and authenticate fake users. The research conducted aimed to identify fake users' behavior. The dataset used contained fake users and authentic users. The research incorporated 17 features to be used. The RF algorithm demonstrates high accuracy in both 2-class classification (authentic and fake users) and 4-class classification (authentic users, active fake users, inactive fake users, and spammers).