# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 3 Related Works
The results obtained showed that the RF algorithm had an accuracy level of 91.76%. Furthermore, it revealed a difference between fake and authentic users. The researchers in [35] introduced different methods for detecting spam accounts on X based on classification and clustering methods. The methods used SVM, Multi-Layer Perceptron (MLP), and RF. RF methods have better results in performance, with an accuracy of 96.2%. A study conducted in [36] proposed identifying the minimal profile data required to identify fake profiles in the LinkedIn platform. The research also proposes an appropriate data mining technique for identifying fake profiles. The techniques use four key data mining ways, i.e., neural networks, SVM, Weighted Average (WA) for a fake profile, and Principal Component Analysis (PAC). The technique incorporated showed an increase in the approximate accuracy by 14%. Collectively, it led to an increase in the accuracy rate of fake profiles on LinkedIn. The study [37] proposed a Deep Profile technique that would incorporate a deep neural network algorithm that would help detect and control the fake accounts in the social media network. The research used a dynamic convolutional neural network (CNN) learning model to classify a fake profile. In addition, it proposed the inclusion of a novel pooling that would help to optimize and enhance performance of neural network's during the training process. The results obtained show a higher accuracy rate with a relatively lower loss compared to common learning algorithms. A study [38] proposed a graphical-based semi-supervised learning algorithm (EGSLA) that would help detect fake users from a large pool of large X data. The proposed method included four main modules: the collection of data, feature extraction, classification of data, and decision-making techniques. The results of the study show that the EGSLA achieved 90.3% accuracy in detecting fake accounts. The authors in [39] proposed a technique to detect malicious and fake user accounts by putting a Chrome extension as a service on a user's homepage on the X platform. The technique is based on ML and graph-based trace and aims to differentiate fake accounts from real ones. The experimental results showed excellent performance, with an accuracy of 99.16%. The study conducted in [6] involved detecting fake and automated accounts that would help detect fake engagement in Instagram accounts. In this research, two key datasets were generated. ML algorithms like LR and neural networks were used. To detect automated accounts in Instagram, a cost-sensitive algorithm technique was applied due to its unnatural bias in the dataset. From the research conducted, automated account detection yielded an accuracy of 86%, and fake account detection yielded an accuracy of 96%. In the study [16], the authors proposed a model to detect account and message spam on X based on ML. The authors use behavioral and content-based features for spammeter detection. The results showed that RF had the best accuracy for detecting spam messages, and LogitBoost (LB) had the best accuracy for detecting fake accounts. The study [40] proposed to incorporate a K-means algorithm that integrates a levy flight using a sinusoidal map to facilitate the tuning of the generated absorption coefficient produced. The study primarily aimed to model the spammers on X using a bioinspired computational method by including 13 modeling factors. The results obtained had an accuracy of 97.98% and a fast convergence rate. The study [41] presents a novel approach for detecting bots on social media platforms, specifically X. It uses a deep learning model based on LSTM architecture, which combines post content analysis with metadata. The model is trained on a dataset of around 3000 X bot examples. The model achieves high classification accuracy, with an accuracy score exceeding 96% for bot detection at the post level. When applied to account-level detection, the accuracy increases to 99%, indicating its effectiveness in distinguishing between bots and human users.