## 4 Proposed Methodology for Rumor Detection
### Machine Learning for Rumor Detection
#### 4.3.2 Neural Network for Rumor Detection  
Neural networks are used in deep learning algorithms to learn about data and classify them. In a typical neural network, each weight has a connection that transforms it and creates a neuron's input. The sigmoid activation function is used in this project, which has two layers. No other research effort has ever taken this method. This is used to determine if neural networks or supervised learning models perform better. A recurrent neural network consists of a network that uses recurrent connections to access memory. In a feed forward recurrent neural network, inputs are independent of one another; however, in an RNN, all inputs are reliant on or related to one another. The structure of RNN is shown in Figure 3.  
Before discussing the RNN, we discuss the feed forward neural network. In a feed forward neural network, there is no memory space for storing the previous state results, which is why a feed forward neural network cannot manage the time series problem. In recurrent neural network, RNN, there is a memory unit that stores the result of previous states as well as that of the current state. The method of RNN is shown in Figure 3. From Equation (10), \(h_{t}\) is the current state, \(h_{t-1}\) is the previous state, and \(X_{t}\) is the current input state. In RNN, we consider the current input state as well as the previous state at the above given equation. This function returns the current state RNN performance function on each input state. In Equation (11), a tanh activation function is used. In Equation (11), \(h_{t}\) is the current input, \(w_{h}h\) is the weight of the previous hidden state, and \(w_{x}h\) is the weight of the current input state. Equation (12) show the output results of RNN: in the output state, the weight of the output state is shown. \[h_{t}=f(h_{t-1},X_{t}) \tag{10}\]  
In Equation (10), \(h_{t}\) is the current state, \(h_{t-1}\) is the previous state of RNN, and \(X_{t}\) represents the current state. In RNN, we use the current input with the previous output state, and f represents some function. \[h_{t}=tanh(w_{hh}h_{t-1}+w_{xh}X_{t}) \tag{11}\]  
In Equation (11), tanh is the activation function, \(w_{hh}\) is the weight of the previous hidden state, and \(w_{xh}\) is the weight of the current hidden state. \[y_{t}=W_{hy}h_{t} \tag{12}\]  
Long Short-Term Memory  
Recurrent neural networks are a type of network that uses recurrent connections to build memory. The notes they receive trigger RNN notes. On the other hand, the LSTM gate accepts or rejects the data based on their weight. Later, these signals become disturbed by their own weight. The RNN learning process then adjusts the weights that regulate the hidden state and input. However, these calls learn when they obtain some information, output some information or delete information through the consequential steps of taking back propagation error. An LSTM unit, unlike a standard recurrent unit (Equations (13)-(18)), preserves a memory cell \(C_{t}\) at time \(t\). The following formulas are used to calculate an LSTM unit's output ht. \[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{13}\]  
Figure 3: Architecture of RNN. \[i_{t} =\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{14}\] \[\widetilde{C}_{t} =tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{C})\] (15) \[o_{t} =\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\] (16) Figure 3.0 (17) Figure 3.1  
A logistic sigmoid function is defined as follows: the amount of new memory added to the memory cell is controlled by the input gate. The extent to which the present memory is lost is determined by the forget gate \(f_{t}\). The memory \(c_{t}\) is updated by forgetting part of the old memory and inserting new memory \(c_{t}\). \(O_{t}\) represent the output gate of a memory as shown in Figure 3.2  
Bidirectional Long Short-Term Memory  
The flow of bidirectional LSTM is not just backward to forward but also forward to backward, using two hidden layers. As a result, Bi-LSTMs have a better understanding of the situation.