# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 2 Related Work
### Machine-Learning Models to Detect Misinformation  
Some basic machine-learning models used for classifying data on the basis of their concepts are recurrent neural networks, support vector machine, k-nearest neighbors, logistic regression, random forest, convolutional neural networks, bidirectional gated recurrent unit, capture, score, and integrate (CSI), sentiment-aware multimodal embedding, hierarchical attention network, and dEFEND. The following briefly explains each ML model. A recurrent neural network (RNN) is a more powerful artificial neural network (ANN)-based computing model that simulates a biological neural-network system and is well-suited for many significant applications. Unlike traditional feed-forward neural-network models, the recurrent layer of RNN has feedback loops. RNN also has a short-term internal memory that allows for it to persist in its state to influence the current output and next sequence of inputs. RNN assumes that the impact of previous and future elements determines the following outputs in the sequence, in contrast with other neural network models. Long short-term memory (LSTM) networks are a particular class of RNN that are capable of understanding long-term dependencies. Special units are also implemented, along with the regular units of recurrent neural networks. LSTM units employ a memory cell that helps information to persist for longer. The data stored in the memory are controlled by a set of gates: input, output, and forget gates. Forget gates decide whether information should be kept or thrown away, and what type of information to be kept. An input gate is responsible for modifying the cell state, and the output gate identifies the next hidden state. The working of LSTM is quite similar to that of RNN, except that it is performed within the LSTM units or cells. Support vector machine (SVV) is a well-known supervised-learning-based classification and regression model used to classify data into a number of different categories [59]. The SVM model tries to identify an N-dimensional hyperplane that categorizes the given data into separate classes. The main objective of an SVM-based classifier is to find the right hyperplane that holds the maximal margin among various hyperplanes that exist for classifying the given data points. SVM is mainly split into two types, linear and nonlinear SVM. A linear SVM classifies linearly separable data points, whereas the latter efficiently mapped nonlinearly separable data values to their corresponding classes. The general form of the classifying hyperplane is given by Equation (1). \[W^{T}x+b=0 \tag{1}\]  
where \(W\) is the weight vector, \(x\) is the input feature vector, and \(b\) is the bias. The new data are classified into class 1 if \(W^{T}x+b\geq 0\), and class 2 if \(W^{T}x+b<0\), or vice versa. SVM can also be used for multiclass classification. The authors in [59; 60; 61] used SVM for misinformation or fake-news detection. The k-nearest neighbor (K-NN) algorithm is used to either classify a data point on the basis of a categorical outcome or to predict a numerical outcome [62]. K-NN is an automated data-driven algorithm that is based on identifying k data points in the training dataset that are similar to a new data point for classification purposes. The neighbors are then used to classify the new data point. The distance between data points is measured using the predictor values to calculate the used measurement distance. Euclidean distance is the most popular for distance measurement among others, such as Manhattan distance, Mahalanobis distance, maximum coordinate distance, and granularity-enhanced Hamming (GEH) distance [63]. If the difference between two points \(x\) and \(y\) was denoted as \(d(x,y)\), a metric conforms to four criteria [64]:  
1. \(d(x,y)\geq 0\); non-negativity
2. \(d(x,y)=0\) only \(ifx=y\); identity
3. \(d(x,y)=d(y,x)\); symmetry
4. \(d(x,z)\geq d(x,y)+d(y,z)\); triangle inequality  
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