# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 5 Results and Analysis
### Comparison with Machine Learning- and Deep Learning-Based Techniques  
A comparison was made between the ML techniques; the comparison aimed to evaluate the performance of the proposed framework. The comparison involved two datasets. The comparison involved the proposed framework against other existing ML models such as GaussianNB (GNB), LR, RF classifier, and SVM. Tables 11 and 12 present a comprehensive evaluation of various ML frameworks, highlighting the performance of the proposed framework. Remarkably, the proposed framework exhibited the best overall performance among the considered frameworks. However, it is noteworthy that the RF classifier demonstrated higher accuracy, specifically in database 1. Due to the structured and non-sequential  
Figure 7: Results on dataset 2: (**a**) shows 50 iterations loss, (**b**) shows 50 iterations of accuracy, (**c**) shows 100 iterations lost, and (**d**) shows 100 iterations of accuracy. nature of dataset 1, it plays to the strengths of the RF algorithm, which can handle such data types efficiently and requires less data for training. Nevertheless, despite its notable results, it can encounter challenges when handling enormous datasets. As the data size grows, the computational and memory requirements of training the RF model can become significant. This can lead to increased training times and resource constraints. On the other hand, the comparative analysis conducted in this study involved the proposed framework and other existing DL models, namely MLP, CNN, and DNN classifiers. Notably, the LSTM-based model showcased distinct performance metrics across two distinct datasets. In the first dataset, the LSTM model outperformed DNN, dynamic CNN, and MLP in terms of accuracy, recall, and F-measure, clearly demonstrating its superior capability. However, in the second dataset, although our model achieved higher accuracy and recall rates, DNN, dynamic CNN, and MLP exhibited better F-measure scores. This discrepancy suggests that, while our model excelled in overall accuracy and capturing true positives and negatives, the competing models demonstrated a stronger balance between precision and recall, yielding higher F-measure scores. It is noteworthy to a discuss related study [28] on Facebook fake profile detection, which indicated that a DNN model outperformed LSTM and Bidirectional LSTM (BiLSTM) models. However, in the instance of Instagram, this research demonstrates that the LSTM model outperforms the DNN. This discrepancy emphasizes the necessity of platform-specific analysis when comparing the effectiveness of various deep learning models for detecting fake accounts. To summarize, the research effectively developed an LSTM-based framework for detecting fake Instagram accounts; when compared to ML and DL methods, they demonstrate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Classifier**} & \multicolumn{1}{c}{**Accuracy**} & **Recall** & **F-Measure** \\ \hline \multirow{8}{*}{ML} & SVM & 93.31\% & 72.2\% & 76.5\% \\  & GNB & 91.92\% & 66.7\% & 71.3\% \\  & LR & 94.  
higher accuracy and recall. This framework effectively used LSTM's capabilities in automatically extracting relevant features to meet the limitations of manual feature engineering. A comparative investigation of two Instagram datasets confirmed the model's effectiveness. This study extremely advances fake account detection techniques by providing a robust solution adapted to Instagram's unique environment. To our knowledge, there are three known approaches, and an analysis of those methods is shown in Table 13. The variability in datasets can significantly affect the reported accuracy, as different data characteristics (such as size, class distribution, and feature richness) can lead to variations in model performance. As a result, the accuracy comparisons across methods may not provide a fully reliable assessment without considering the impact of the dataset differences.