# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 4 Proposed Framework
### Stage3: Framework Structure  
The LSTM technique will be used at this stage, and the information that has been pre-processed will be entered. In order to extract the output, the framework will process data using LSTM. LSTM is a special type of recurrent RNN. It refers to the series of neural networks that works by processing sequential data. LSTM has three gates, as shown in Figure 5. The three \"gates\" are serially arranged in an LSTM unit. It has an input gate, forgetting gate, and output gate. When a piece of data is added to the LSTM network, it is \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Feature Name** \\ \hline profile Pic & Whether the account have profile pic or not. \\ nums/Length Username & The percentage of a username’s length to its number of \\  & numeric characters. \\ fullname Words & Whole name in word tokens. \\ nums/Length Fullname & The percentage of the number of numeric characters in \\  & whole name to its length. \\ name = =Username & Username and whole name are equals. \\  & description Length & User bio length in characters. \\ external URL (Uniform Resource & Whether the account have an external c or not. \\   Locator) & \\ Private & Whether the account is private or public. \\   Posts & Total posts number. \\   Followers & Follower count of the account. \\   Fake & True, if account is a spam/fake account, False otherwise. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of user features in dataset 2.  
carefully selected by the rules. Information that adheres to the algorithm rules is the only one left out. The rest is sent to the forgetting gate. The work of the gates is to allow the information to be passed selectively. The gates allow passage of information selectively in an ordering manner. Equation (1) illustrates the LSTM network's default activation functionality and the sigmoid function. The gating unit has the capability of adding or deleting information. \[\text{sigmoid layer }\sigma(\mathrm{x})=\frac{1}{1+e^{-x}} \tag{1}\]  
The sigmoid neural network layer and the multiplication pair operation helps to determine whether the information passes or not. The sigmoid layer consists of an element output that is represented by real numbers between (0.1) that represent the weight of the information passing. The LSTM neural network consist of a layer that has the function of \(\tanh\) activation. This is shown in Equation (2). The \(\tanh\) is used for updating the state of neurons Equation (2). \[\tanh\left(\mathrm{x}\right)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{2}\]  
The LSTM cell's information flow, as shown in Figure 2, can mathematically be described. The symbols \(\oplus\) and \(\otimes\) denote the adding and multiplying value of the framework, respectively, while the arrows denote information direction of flow. The first layer of the memory gate helps determine how to remove any information that is not necessary from the cell state. This can mathematically be written as  
\[\mathrm{f_{t}}=\sigma(\mathrm{W_{f}}\times\mathrm{x_{t}}+\mathrm{U_{f}}\times \mathrm{h_{t-1}}+\mathrm{b_{f}}) \tag{3}\]  
where \(\mathrm{f_{t}}\) represents the forgetting threshold at time \(\mathrm{t}\), and \(\sigma\) represents the sigmoid activation function. \(\mathrm{W_{f}}\) and \(\mathrm{U_{f}}\) represents the weights, \(\mathrm{x_{t}}\). represents the input value \(\mathrm{h_{t-1}}\) and represents the output value at time \(\mathrm{t-1}\), and \(\mathrm{b_{f}}\) represents the bias term. The function of the second input gate is to determine which specific information from the current input vector should be saved in the cell state. Then, it assists in updating the cell state value, and the \(\tanh\) layer helps generate the new value state of \(\mathrm{C_{t}}\).