An Approach to Detecting the Spread of False Information on the Internet Using Data Science Algorithms

Today we are all witnessing the rapid immersion of society into the digital world. The amount of information is huge, and it is often difficult to distinguish normal news and comments from unreliable information. In this regard, the issue of detecting fake news and countering its spread becomes urgent. This task is not trivial for the following reasons: firstly, the volume of content that is created every day on the Internet is enormous; secondly, the detection system requires news plots that are obviously true; thirdly, the system must be able to analyze information in close to real time. The article presents a new approach to detecting the spread of false information on the Internet based on the use of data science algorithms. The concept of a fake news detection system includes 4 components and a data storage system. The article presents an experimental evaluation of methods implemented in the framework of the neural network training component and the detection of false information