# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 2 Related Work and Motivation  
Due to the dynamicity and extremely complicated and varied online data on media platforms, finding and analyzing reliable information is a significant problem. Ref. [1] proposed a method for rumor identification at an early stage. For the purpose of early rumor identification, these authors integrated Reinforcement Learning (CM) with a recurrent neural network and took into account datasets from X (Twitter) and Weibo. The increased performance of 93.3% and 85.8% is on par with modern rumor detection techniques. To identify fake news, ref. [2] employed a basic Bag of Words vector, Continuous Bag of Words, and Skip-gram. The authors employed various machine-learning algorithms for classification, and 95.49 percent accuracy was achieved by combining text-based characteristics and stylometric features. Using a deep neural network, ref. [3] was able to find bogus news quickly. The accuracy of the suggested method was roughly 90%. Recurrent neural networks were used by [4] to identify news rumors. The authors evaluated the performance of various evaluation parameters. The datasets were created using real-time tweets from PHEME. Ref. [5] discussed various challenges in the difficulty in obtaining high-quality labeled datasets for online false news detection, and the difficulty in predicting fake news in advance. In order to combat false news, ref. [6] advocated for the identification of fake news based on content, user input, and intervention. The authors also talked about the difficulties in developing quantitative techniques for analyzing fake news. The author of this study also discussed several research topics, including dynamic knowledge bases, fresh intervention techniques, and datasets for intent detection. Ref. [7] suggested several methods for detecting rumors, as well as determining the right datasets to use for these tasks. For the purpose of identifying false news in online web media, ref. [8] applied 23 supervised classification algorithms to a structured news dataset. Sequential Minimal Optimization surpassed the others in accuracy and F-score. In the future, supervised algorithms may be hybridized and combined for improved outcomes. Ref. [9] suggested propagation-based methods that are resilient to attacks and independent of language. The topic of numerous study gaps, difficulties, and potential future areas of information pollution in social media was covered in [3]. A false news extraction and detection system was put forth by [10] using text present in images. The existing system does not address local news, and the text present in the image is a significant problem because of the shadowing effect. In order to validate the accuracy of the text in the image, the text was extracted from images and the reality parameter was calculated. In 2018, ref. [11] published a survey paper on the identification of rumors. The authors covered a variety of research topics, including timely identification of rumors, investigation of fake news based on deep learning techniques, investigation of fake news across multiple domains, and identification of auditable content. Ref. [12] worked with artificial intelligence to propose four key parameters for accepting fake news and to defeat and protect against these parameters. Fake news detection is a very new area for artificial intelligence research and implementation. Ref. [13] identified the roots and historical patterns of social media misinformation. Approximately 300,000 tweets can be retrieved from X (Twitter). A linguistic approach can be considered for detection, to distinguish the type of news in different news articles. Ref. [14] introduced two new datasets by crowdsourcing to develop an automatic recognition system. Ref. [15] proposed a new dataset, called LIAR, to detect fake news, and a novel hybrid CNN was developed to integrate metadata with text. This dataset is used for policy research on perspective classification and argument mining. Ref. [16] described a CSI model that was collected, scored, and integrated to detect fake news.