Fraudulent Information Prediction using Block Chain Technology and Machine Learning

Social media has proven to be the most effective tool for disseminating knowledge in recent years. As the amount of information available expands, it becomes more vital to read the right news, and recognizing fake news that provides wrong or incomplete information becomes more important. It takes a long time for individuals to check the accuracy of news. The collection of phony word data assists in the detection of spam tendencies in the news. By altering the content of a real news feed in fake news, those with malicious intent spread misleading information. In order to spot fake news, it's necessary to understand how rumors spread. Under the current system, people must quickly assess a rumor and decide whether or not to spread it. Nonetheless, it is difficult to make an informed decision and select the greatest choice without a thorough investigation. People do not have the time to learn more or reason. In addition, if the rumor remains, the user will become suffocated and will never change. People may have different viewpoints on the same incident, such as favorable, dubious, and negative. People who have doubts about their convictions, on the other hand, have a limited vocabulary. To address this problem, it is critical to employ decentralized technologies. This functionality is generally available in block chain technology. Block chain can be used to track and control information flow. The proposed model utilizes the credible news sources, such as a real-time search engine. Block chain technology ensures the integrity of news submitted on social media. In the real-time system, users can upload news in the form of images, material, and hash tags. These facts are passed on to the validator, who will determine if the data is fraudulent or not. To protect the data, the information is subsequently converted to a block chain. This approach aids in determining the authenticity of the news by using real-time synchronization.