Effective prediction of fake news using a learning vector quantization with hamming distance measure

It never happened before in human history the spreading of fake news; now, the development of the Worldwide Web and the adoption of social media have given a pathway for people to spread misinformation to the world. Everyone is using the Internet, creating and sharing content on social media, but not all the information is valid, and no one is verifying the originality of the content. It is sometimes complicated for researchers and intelligence to identify the essence of the content. For example, during Covid-19, misinformation spread worldwide about the outbreak, and much false information spread faster than the virus. This misinformation will create a problem for the public and mislead people into taking the proper medicine. This work will help us to improve the prediction rate. The proposed algorithm is compared with three existing algorithms, and the result is better than the other three current algorithms. The prediction rate of impact for the proposed algorithms is 93.54% © 2022 The Authors