# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 2 Related Work and Motivation
The RNN was used to record user temporal patterns in the first module, the next module is used to analyze user behavior, and the other module was used to classify fake news and integrated the first and second modules. The authors also addressed research questionsfocused on the concepts of reinforcement learning and crowdsourcing in models. Ref. [17] discussed various research gaps and possible research directions in the area of fake news characterization and detection on media platforms. Ref. [18] conducted the hybridization of RF and SVM models which outperforms the performance of individual models during the categorization of positive or negative sentiment reviews, during the identification of product reviews offered by the Amazon datasets. Considering the datasets of 500 Amazon products, the individual model's accuracy ranges around 80% to 82%, which further increased to 86% during the hybridization. CNN and SVMs were used together as a hybrid approach in [19], in order to detect and classify different kinds of orange diseases. In the blended model, different disorders were identified such as Penicillium, Scab, Melanose, or citrus canker. CNN used for derivation of features and SVMs for the further classification enhances the accuracy of the hybrid model up to 88%, resulting in a significant improvement with respect to using the models separately. Looking at the continued improvement in results due to the hybridizations of different deep learning models in terms of product reviews or categorization of diseases in oranges, researchers further explored this process in alignment to the specific topic. [PERSON]. [20] proposed the hybridization of CNN and RNN models for the classification of fake news. Further, the model was investigated and validated on different datasets (FA-KES and ISO), achieving excellent results in terms of accuracy, recall, and precision with respect to the baseline model evaluations. During the analysis behind the need for hybrid models, many researchers came to conclusion that in baseline models, it is very difficult to cater the rapidly changing strategies used by the people who create and share false information, as language and compositional analysis is insufficient and inappropriate. [PERSON]. [21] derived a hybrid approach of using SVM and KNN models to overcome this problem, which successfully identified bogus news. In order to further ensure the robustness and generalization of the results, a cross-validation process must be applied as this prioritizes the complex relationships of models. This holistic approach provides a complex display of different attributes comprising the behavior of users and the dynamics of social media posts. The continuation, evolution, and impact of cross validation models emerges at large, in terms of different evaluation parameters, in comparison to the baseline models. Hybridized models of CNN and SVMs, RNN and SVMs, or different models, was carried out as mentioned in the literature. During the study, it was observed that hybridization impacted the results at large in different scenarios and on different evaluation parameters in comparison to sole models. Accuracy of fake news classification reached around 88% [20; 21]. Our proposed approach derived excellent results in comparison to the mentioned literature and with improvements in different evaluation parameters, varying from 4% up to 10% in different scenarios. Our hybrid approach involved a categorization approach of different outliers at each layer to combat the pervasive spread of misinformation in our rapidly growing digital era. Our model analyzes the highly dimensional data of different attributes, further dividing it into news categories, which results in a tree-based structure that brings optimality, and further enhancing optimality through the binary classification of the datasets, which is very well supported through SVMs. Fake information can be created and disseminated very easily through media platforms, resulting in real-world impacts.