# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Applied Methods
#### 2.3.2 Classifying Methods  
Random forest (RF) is an ensemble learning method that uses a combination of multiple decision trees when training. When used in a classification task, RF generates an ensemble of trees that predict the classification result by casting a vote to determine the most popular class. In RF, each tree in the ensemble is built from a sample drawn with a replacement from a training set--which, in the case of our experiments, are the datasets that we used [37]. The research conducted by [PERSON], et al. [10] and [PERSON], et al. [27], which we discussed more in detail in Section 2.1, showed that ensemble learning methods achieved high accuracy scores for false news detection. This provides a reason why we chose to include RF as one of the methods for our classifying experiments. Naive Bayes (NB) methods are a set of supervised learning algorithms based on applying [PERSON] theorem with strong (naive) independence assumptions between features given the value of a class variable. In this experiment, we used the MultinomialNB algorithm in our classification experiments. MultinomialNB predicts the probability of general labels for a given text, applying those features in a multinomial function based on [PERSON] theorem. NB algorithms are often used as a baseline for text classification tasks; they work well with smaller datasets and are faster to train when compared with other popular classifiers [38]. Logistic Regression (LR) is a statistical model that calculates the probability of a binary outcome, based on prior observations from a dataset. LR can consider multiple input criteria for the eventual outcome of a prediction. In the instance of false news detection, the LR model could be capable, for example, of taking into consideration the history of label distribution (true/false) of news articles written by a reporter. Based on the historical data, the LR model calculates the score for a new case based on its probability of receiving either one of the labels [39]. LR has been successful for false news detection purposes previously [31; 33; 40]. Support vector machine (SVM) is a machine learning algorithm that uses supervised learning models to analyze data for classification, regression, and outlier detection. SVM works by creating a hyperplane that separates the used training data into classes, e.g., false/true. The new data are then fitted into the same space as the old data, and the class prediction for the new data is determined by which side of the previously created hyperplane they fall [41]. SVM has been used widely in false news detection, often being able to receive high accuracy results [23; 42; 43]. k-Nearest Neighbors (kNN) is a non-parametric supervised learning method used for classification and regression tasks. The kNN classifier takes the \(k\) closest training data as an input, and the input data are then classified based on a plurality vote of their neighbors, meaning that an object currently being classified is assigned to a class according to its \(k\) nearest neighbors, where \(k\) is a positive integer [44]. In our research, we used \(k=3\), where an object is classified according to what is the most common class among its neighbors. Like NB, kNN is often used as a baseline for text classification tasks and is fast and simple to train [38]. Multi-layer Perceptron (MLP) is a feed-forward artificial neural network, typically consisting of three different interconnected layers: input layer, hidden layer, and output layer. In MLP classification tasks, the input data are passed through the layers, where each layer solves a specific part of the task. The output of the solved result is passed through the layers until the result is determined, e.g., whether an article is false or not [45].