Methodological and technological foundations for countering cognitive threats in generative multimodal content

The rapid development of artificial intelligence platforms, methods, and technologies creates favorable conditions for the formation of destructive multimodal content and its controlled distribution across various information resources on the Internet. The article presents original research results and develops an approach to countering a new class of information threats — cognitive threats in generative multimodal content. For the first time, a cognitive threat model has been substantiated that takes into account the specifics of a new class of attackers — cognitive security violators. New scientific results have been obtained through training and applying scientifically validated classes of neural network models to analyze various types of emotions. Their novelty and practical value are related to the applicability of these models to solving problems of detecting various types of cognitive biases in the generative multimodal content of Telegram channels.