Large language models for processing and intellectual large volumes heterogeneous texts analysis with identifying bots in social networks

Abstract

The paper describes the problems of analyzing and processing large volumes of heterogeneous texts in natural language in the task of identifying bots in social networks based on deep transfer learning methods, in particular large language models. An analysis of the specifics and key aspects of text content structuring, processing and analysis is provided, the relevance of the problem is substantiated, an analysis of existing approaches in the scientific literature is carried out, the advantages and possibilities of using artificial neural networks and machine learning to automate the processes social network users texts posts analyzing are listed. The set of input data selected for research is described, the choice of artificial neural networks language models is justified and the specifics of using transfer learning to adapt models to the bot search task are described. The technical means and services for implementing the work of the created web application are described, object-oriented models of the system are developed using the UML language in the form of use cases and components diagrams web application, software functionality, prototype pages and a user graphical interface are developed. The results of experimental studies of selected language models on an expanded input data set in modes with and without text explanations are presented. At the selected post, an analysis adapted neural network models results and work specifics was performed, promising ways for further research and development of the identified problems were identified.