# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
## 2 Related Work  
Online social networks such as Instagram, Facebook, Google Plus, Twitter (X), LinkedIn, Pinterest, etc., are growing platforms [12] because users can utilize these platforms, without paying anything, to communicate with other users and share their information [13]. The current statistics show that there are about 349 million Facebook users in India and 194 million registered users in America [14]. The same trend has been found for Twitter, which has about 145 million daily users and 330 million monthly users. A similar type of trend has also been observed on Instagram and YouTube. Discovering and removing fake users in social networks improves the security of users in these networks and increases interest in using these networks. Therefore, this issue has become one of the most challenging research issues in the field of social networks, and many researchers have presented methods to solve this problem. A number of researchers have applied machine learning techniques to various Online Social Network (OSN) platforms such as Twitter (X), Facebook, and Instagram. For example, SVM, Random Forest, Logistic Regression, J48, Naive Bayes, Neural Network, and KNN methods have been used to detect fake Twitter accounts [2, 15, 26, 25, 2, 16, 2, 2, 17, 2, 23, 24, 25, 26]. Equally, feature reduction and correlation techniques have been used to increase the accuracy of the classifiers in [2, 15, 26, 23, 25]. Similarly, for the detection of fake accounts on Facebook, several classifiers have been used by researchers, including SVM, Random Forest, Neural Networks, Naive Bayes, and KNN, as seen in [27, 28, 29, 30]. Entropy and information gain was applied in [29] to reduce the features. Fake accounts on Instagram have been identified using various machine learning methods, including Random Forest, Logistic Regression, Naive Bayes, SVM, Neural Networks, and KNN, as seen in [3, 4, 6, 7, 8, 31, 32]. The Instagram dataset with fake accounts was purchased from different sources and authentic users in [3]. Based on these sources, seventeen features have been previously used, as follows: six metadata features, three media information features, two interaction features, two media tag features, and four media similarity features. In addition, a cost-sensitive genetic algorithm to deal with abnormal orientation in the dataset was used in [31]; equally, an approach combining image recognition using CNN and natural language processing was used to identify fake accounts on Instagram in [6]. A dataset of legitimate and fake accounts was first created in [8], and classifiers were then applied to detect fake accounts. Multiple stages of Instagram data processing were applied in [32], which include data preprocessing, model selection, and evaluation of the classification algorithms. Table 1 gives a comprehensive explanation of the datasets, performance, and limitations of these works. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Data** & **Methods and Performance** & **Limitations and Research Gaps** \\ \hline \multirow{4}{*}{[PERSON] et al. [15]} & \multirow{4}{*}{Twitter (X) accounts and bots} & Feature selection with SVM, & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\  & Neural Networks, and a & \\  & & combination of SVM-NN with & \\  & & accuracy of 98\% in & \\  & & training dataset & \\ \hline \multirow{4}{*}{[PERSON] et al. [16]} & \multirow{4}{*}{Twitter (X) accounts and bots} & SVM, Logistic Regression, & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } \\  & Random Forest, and KNN with a & \\ \cline{1-1}  & & peak accuracy of 98\%. Used & \\ \cline{1-1}  & & Z-Score and Min-Max & \\ \cline{1-1}  & & normalization & \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & \multirow{4}{*}{Twitter (X) accounts} & SVM, Decision Tree, Random & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & & Forest, Naive Bayes with an & \\ \cline{1-1}  & & accuracy of 97\% for SVM & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the literature.