Devising a code-free method for detecting signs of informational-psychological influences in messages

This study investigates text messages that potentially contain signs of informational-psychological operations (IPSOs). The task addressed aims to solve the problem of detecting signs of IPSOs in the media space. An innovative method for detecting such signs has been proposed, based on the construction and analysis of semantic networks and implemented without the use of program code by using large language models (LLMs). This makes it possible to generate formalized analytical queries to LLMs in the form of a code-free system based on the composition of structured prompts. The method's unique feature is the parallel analysis of data from two sources of knowledge: internal and external. The internal one contains generalized IPSO patterns formed on the basis of a wide corpus of data. The external one includes verified examples of fake messages from social networks, news outlets, and archives of fact-checking organizations. To improve the accuracy of analysis, semantic normalization of concepts is used, which employs embedded vectors to unify terminology, as well as comparison of causal paths in semantic networks to identify connections. The assessment of the probability of a message belonging to IPSO is formed by aggregating the results using a weighted average, which makes it possible to take into account semantic and structural similarity. An example of applying the method to the analysis of a disinformation message is given, demonstrating the ability to detect key signs of psychological influence: manipulative narratives, emotional loading, and cause-and-effect relationships. The proposed method is flexible, reproducible, and accessible to researchers without programming skills, which makes it a valuable tool for monitoring information threats and analyzing disinformation in the context of information confrontations