# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 6 Conclusions and Future Work  
This paper introduces the first utilization of LSTM in Instagram for fake account detection. The experiments conducted using the real dataset for the Instagram accounts show that the proposed framework effectively and efficiently detects fake accounts. The performance evaluation and comparison with other ML models showed the superiority of the proposed framework, achieving an accuracy of 97.42% for the first dataset and 94.21% for the second dataset.  
Furthermore, the framework underwent comprehensive testing using an X dataset, resulting in an impressive accuracy of 99.42%. Including the X database in the evaluation enabled the assessment of the model's generalizability and robustness across diverse social media platforms and yielded valuable insights into its performance. These results collectively underscore the robustness and versatility of the proposed model, positioning it as a promising solution for detecting fake accounts across various social media platforms.  
Building upon these promising outcomes, several avenues exist for future research and development in fake account detection on social media platforms. While our framework exhibited remarkable accuracy on Instagram and X data, it would be beneficial to extend its assessment to a broader spectrum of social media platforms, each with its unique user behaviors and characteristics. Finally, investigating the potential integration of additional data sources, such as user interactions, geographic information, and content analysis, could provide richer contextual information for improved accuracy.  
Conceptualization, M.A.R., N.A., B.A. and G.A.; methodology, M.A.R., N.A., B.A. and G.A.; software, N.A., B.A. and G.A.; validation, M.A.R., N.A., B.A. and G.A.; formal analysis, M.A.R. and N.A.; investigation, M.A.R., N.A., B.A. and G.A.; resources, M.A.R. and N.A.; data curation, N.A.; writing--original draft preparation, N.A., B.A. and G.A.; writing--review and editing, M.A.R.; visualization, N.A., B.A. and G.A.; supervision, M.A.R.; project administration, M.A.R.; funding acquisition, M.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are available at [[https://github.com/BashayerAlkalifah/Fake-Instagram-Account-Detection-/tree/main](https://github.com/BashayerAlkalifah/Fake-Instagram-Account-Detection-/tree/main)]([https://github.com/BashayerAlkalifah/Fake-Instagram-Account-Detection-/tree/main](https://github.com/BashayerAlkalifah/Fake-Instagram-Account-Detection-/tree/main)) (accessed on 7 April 2024).  
The Researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2024-9/1).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Classifier** & **Accuracy** & **Recall** & **F-Measure** \\ \hline \multirow{8}{*}{ML} & SVM & 77.50\% & 72.50\% & 78.80\% \\  & GNB & 92.88\% & 99.50\% & 94.10\% \\  & LR & 96.22\% & 99.40\% & 96.80\% \\  & RF & 99.16\% & 99.00\% & 99.30\% \\  & KNC & 93.08\% & 93.80\% & 94.00\% \\  & DT & 99.00\% & 99.10\% & 99.10\% \\  & LDA & 98.72\% & 98.80\% & 98.90\% \\ \hline \multirow{2}{*}{DL} & Proposed & \multirow{2}{*}{99.44\%} & \multirow{2}{*}{99.12\%} & \multirow{2}{*}{99.52\%} \\  & framework & & & \\ \end{tabular}
\end{table}
Table 15: Comparison of ML results with proposed framework of dataset.