# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 2 Related Work and Motivation
### Proposed Algorithm  
SVMs are used for binary classification, extending the work to multi-class classifiers by the researchers, as well as working well for the detection of rumors, especially X (Twitter). This algorithm performs preprocessing for the tweets by converting the jargon words used into the standard forms. Further, regular expressions converted the redundant letter words into original words along with the segmentation of words. During the extraction process of the features, structural, user, and other content about the tweet are also considered. Random Forest is the strongest classification algorithm which classifies large datasets more accurately and precisely. RF is the tree structure, where each tree possesses random vector values and functions as a classifier. This creates various decision trees during the training of datasets, and uses the same for forecasting the data of each individual tree and selection of attributes randomly. Further, RF will result in higher accuracy and robustness by the segregation of multiple trees. Due to the training of multiple trees, variance and standard deviation of each individual tree is reduced, which further enhances the performance. RF removes overfitting and further improves generalizability. For example: consider that the number of trees used is 100, due to a smaller number of trees leading to the over-fitting of the data. In this instance, the random state value is set to 42, which makes the algorithm deterministic, losing its nature of non-determinism. This means that the algorithm will derive the same outcome again and again, and this kind of consistency will further maintain transparency and use in testing. The basic reason behind the hybridization of RF and SVMs is to predict different types of highly dimensional fake news datasets. In this, we divide the datasets of different news categories into multiple classes where the tree-based approach will bring optimality and further reduce the binary classifications which will be further supported in an optimized way by the SVMs. Random Forest results are exemplary when the news categories provide data with a mixture of numerical and categorical features, along with features on a large scale. SVMs maximize the margins and rely on the concepts of distance between two different points. Furthermore, min-max and different scaling processes are used during the pre-processing step. In the proposed work, the larger the size of the datasets in future research, the more optimized the results of this hybridization model will be, so we started the research with small datasets in order to understand the different types of computational trade-offs which can be resolved in the future. Due to the cross validation of the machine-learning models, there is significant improvement in precision, accuracy, recall, and F1-score in comparison to individual models, as tested on datasets of different types of news categories. The hybridization of the RF and SVM models improves the execution time, that is, the algorithm runs fast in comparison to that of the individual performances, improves interpretability, and also nonlinear dependencies are catered to in the best possible way. In addition to this, as we increase the size of the datasets to greater than 10,000 samples in future work, cross validation of both of the models will produce exemplary results. The process used for the development of the hybrid RFSVM model is as follows:  
Step 1: Identify the spreading of fake news, especially on X (Twitter). In addition to this, try to select the data source containing fake news content, which can further be used for the collection of data and its processing. Step 2: After collection, preprocessing and cleaning will be carried out. Initially, conversion of the datasets from json format to csv format is carried out, containing user tweets along with the reactions against each tweet. The combined datasets will be loaded into the data frame using pandas.