# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 2 Related Work
### Machine-Learning Models to Detect Misinformation
[65] proposed a semantic K-NN (SK-NN) algorithm to overcome the poor accuracy of the traditional K-NN for large volumes of multicategorical training datasets, which scans the entire dataset and categories to classify a single data point. Thus, the SK-NN improves ML performance by using a semantic itemization and bigram model to filter a training dataset. It is a probability-based machine-learning algorithm for classifying data into two or more discrete classes. As the name suggests, it generates a regression model for estimating the probability value of the data point, represented by an n-dimensional vector to be classified into class 1. It considers dependent and independent variables in a data point as a linear function given by Equation (2). \[y=\beta_{0}+\sum_{i=1}^{n}\beta_{i}x_{i} \tag{2}\]  
where X = \((x_{1},x_{2},...,x_{n})\) is the n-dimensional feature vector, \(\beta_{0},\beta_{1},...,\beta_{n}\) are regression coefficients, and \(y\) is the dependent target value. Logistic regression is a special case of linear regression that works on the basis of logistic function, also known as a sigmoid function. The sigmoid function is an S-shaped curve that lies between 0 and 1. Equation (3) represents the sigmoid function. \[p(y)=\frac{1}{1+e^{-y}} \tag{3}\]  
In a binary classification scenario, let \(p(y)\) be the probability that, with a given data point, X is mapped to class 1; then, \(p(y)-1\) represents the probability that X belongs to class 2. Logistic regression models can be divided into three different types on the basis of the number of distinct classes. They are binomial (binary classification), multinomial (multiclass classification), and ordinal (ordered multiclass classification). There are a variety of works in fake-news detection using linear regression [66; 67; 68; 69]. Random forest is also a supervised learning classifier based on decision tree (DT) [61]. It is a simple, flexible, and widely used machine-learning algorithm for classifying documents or other data. As the name suggests, it forms diverse decision trees and trains this model in bagging techniques. Each decision tree is trained and tested by using its own sample data values. Lastly, results are combined, and the best solution is identified by using a voting method. The random forest randomly selects samples of the dataset, builds a decision tree for each sample, and makes predictions on each decision tree. The voting algorithm on these results performs a significant role in selecting feasible solutions from these predictions, which is considered to be the final classification result. A convolutional neural network [70] is a deep-learning-based neural network used to classify texts, images, speech, and so on. It is mainly used for image classification. The convolutional operation is performed in these neural networks. It is a feed-forward neural network, and its behavior is inspired by the visual cortex of animals. Hidden layers are known as convolution layers. The multichannel convolutional neural network (MC-CNN) is mainly used to capture temporal correlations by forming three convolutional layers with kernels along the time axis [71]. It strengthens the connection between features, which improves robustness against errors [72]. However, the MC-CNN ignores spatial correlations and considers single-sensor temporal correlation, which might limit performance as more features are present in the dataset. A bidirectional gated recurrent unit (BiGRU) is a model for processing a sequence, and it consists of two GRUs. One is used for accepting input in the forward direction, and the other is to accept input from the backward direction. BiGRU is considered to be a bidirectional RNN with only forget and input gates [73]. [PERSON] et al. [74] proposed a new hybrid model based on deep learning for fake-news detection, commonly known as capture, score, and integrate (CSI). This model considers the characteristics of users, articles, and group characteristics of users who transmit misinformation or fake news.