# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 2 Related Work and Motivation
### Proposed Framework  
Figure 3 shows the various steps, such as data acquisition, data labeling, definition of the feature set, classification, and calculation of evaluation parameters.  
* Data Acquisition: Authors acquired the dataset about fake news on COVID-19 on X (Twitter) from dataworld.com.
* Data Labelling: Data containing tweets from X (Twitter). After cleaning the records, we labeled them 1 (fake news) and 0 (true news). The training set contains 60% data, and the test set contains 40% data containing fake news related to COVID-19.
* Defining the Feature Set: The set of features that authors used consisted of a TFIDF feature vector [22].
* Classifier: The authors considered naive Bayes [23], Support Vector Machines [24], Random Forest [25], XGBClassifier, and hybrid SVMs and Random Forest.  
The naive Bayes (NB) algorithm, which makes use of the explicit premise that enables a dependency of one another and [PERSON]'s rule, is a simple-to-learn probabilistic algorithm. NB calculates posterior probabilities \(\mathrm{P}(\mathrm{y}\,|\,\mathrm{x})\) for each classification for a specific object \(\mathrm{x}\) based on training data. Applications involving categorization can make use of estimates. NB seems to be a workable solution in many actual applications due to its computational efficiency and many other desirable qualities [26].  
The foundation of Support Vector Machines (SVMs) is the notion of learning statistics. Here, SVMs have been used by numerous academics in data categorization and pattern recognition applications. Theoretically, the SVM concept can be described as follows. (1) A notion that indicates the degree of risk or chance of learning error is known as structural risk  
Figure 3: Proposed framework.  
Figure 2: Research methodology.  
minimization. The decision-making function is established by the SVM learning process to reduce error rates. The fundamental ideas underlying vector machine technology are core capabilities, in order to generate a nonlinear decision function on the data in the preceding space. (2) SVM learning involves dividing the data into two groups and finding the level with the largest margin that can solve the overfitting problem [27].  
One of the strongest algorithms for classification jobs is Random Forest (RF). Large datasets can be accurately and precisely classified using RF. Each tree in the RF system uses random vector values and functions as a classifier. RF creates decision trees during training, forecasts the results of each tree using training data from bootstrap examples, and attributes the selection at random during tree induction. Decision trees are averaged or combined to create predictions using majority voting [28].  
A recursive partition of a data space serves as the XGBoost representation of its tree-like model. Highly recognizable nodes that form a rooted tree make up a decision tree. The root node of the tree at the top has no outgoing branches, connections, or edges. For every other node, there is one incoming edge. Intermediate nodes are marked with outgoing edges. Nodes are at the leaves and decision nodes at the lowest level [29].  
* Evaluation Parameters for Fake News Detection: Various evaluation parameters are used by the author in the manuscript, as follows. \[Precision=\frac{|True\ Positive|}{|True\ Positive|+|False\ Positive|}\] (1) \[Recall=\frac{|True\ Positive|}{|True\ Positive|+|False\ Negative|}\] (2) \[Accuracy=\frac{|True\ Positive|+|True\ Negative|}{|True\ Positive|+|True \ Negative|+|False\ Positive|+|False\ Negative|}\] (3) \[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall}\] (4)