INTRODUCTION
A large number of people are using the Internet as one of its most important inventions. They use the Internet for various purposes. They can access different social media platforms. Through these online platforms, anyone can post or spread news. This platform does not verify the users or their posts ( Ahmed Et al., 2021). This results in some users spreading fake news. ’Fake news’ can be used as propaganda to attack an individual, a society, an organization, or a political party (Manzoor et al., 2019). All this fake news is impossible for a human to detect. In order to identify fake news, algorithms must use machine learning. This paper aims to construct a machine learning model that can predict which Tweets are about real disasters and which ones are not. The dataset contains 10,000 tweets that have been manually classified.(al. & Al Ayub Ahmed Et al., 2021)
In order to optimize revenue, the application helps organizations predict which articles will be popular so that their targeted advertising campaigns can be optimized. (An overview of Random Forest Algorithm in Machine Learning, 2020) The Random Forest Algorithm is a technique of making a classification by using a bundle of decision trees. As well as avoiding overfitting, it is also considered to be an effective technique. It reduces overfitting and helps improve accuracy in decision trees. It can be applied to both classification and regression problems. Continuous and categorical data can both be used. Automatically fills in any missing values present in the data. As a rule-based approach is used, there is no need to normalize data. While random forest algorithms have many advantages, they also have some disadvantages. These algorithms require time and resources with high computational power as it builds numerous trees to combine their outputs. As many decision trees are used to determine the class, it also takes a lot of training time. Due to the number of decision trees, it is also difficult to interpret and fails to determine the significance of each variable. The definition of fake news generally refers to something that is verifiably false and intentionally so. Bias news does not fall under this definition. A biased report can be influenced by the individual’s opinion, but fake news is fabricated intentionally. Their high level of social engagement is one of the most important factors behind the success of fake news stories. Facebook and Twitter give us access to other like-minded individuals. Whenever we read a sentence or a paragraph, our brains incorporate the information with the entire document and understand what the words mean. We teach a system to read and understand fake news with Machine Learning concepts (Shu and Liu 2019). The spread of false information and hoaxes online is on the rise as a result of the advancement of technology. Popular online platforms like social media and the Internet are popular sources of fake news. Various methods and tools have been used to detect fake news, including those that use artificial intelligence. Fake news, on the other hand, aims to fool readers into believing false information, which makes these articles difficult to comprehend. Machine learning cannot effectively detect fake news because the rate of producing digital news is high and runs at every second. (Carlson 2017). Due to the emergence of social networking sites, it has become possible to analyze the prevaIn developing countries like India, it is important to stop rumor-mongering and to focus on providing accurate, reputable news articles. (Jain et al., 2019)lence of fake news using new communication methods. This project aims to develop a method for detecting and removing false or misleading information from websites that users can access. By analyzing a few simple elements of the title and post, we can tell whether a post is fake or not. Authenticating news and articles appearing on social media sites such as WhatsApp groups, Facebook pages, Twitter, and other blogs and social networks is a question. Society is harmed when rumors are believed and are portrayed as news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles.(Jain et al. 2019)