Social media automated account (bot) detection system architecture

Abstract

Background. Social networks are one of the main sources of information. However, as trust in them grows, so does the use of automated accounts to spread disinformation and influence public opinion, which is an effective method of manipulation. Automated account detection systems are used to detect such accounts. The purpose of the article is to analyze the architecture of automated account (bot) detection systems in social networks and to identify the main directions for improving such architecture, taking into account the identified shortcomings.
Methods. Methods of analysis, systematization, and generalization were used to identify shortcomings and areas for system improvement, and a modeling method was used to develop generalized and improved system architecture.
Results. An improved architecture of a system for detecting automated accounts (bots) in the social network x.com is proposed, which uses a mixed model of data analysis using artificial intelligence and combines web scraping and API queries for data collection.
Conclusions. The general architecture of automated account (bot) detection systems in social networks was analyzed and the main shortcomings and areas for improvement were identified, among which the following can be distinguished: restrictions on API requests from social network owners, unstructured and free style of posts in social networks, and constant changes in post generation algorithms by automated accounts. An improved architecture of the automated account (bot) detection system was proposed, which combines web scraping and API requests for data collection and allows parallel execution of social network data analysis.
Keywords: сomputer system, data collection, data analysis, machine learning.