Model development of the system for avoiding echo chambers in social networks

Abstract

This article examines the problems that appear in the process of the development of social networks. The focus of the study is on the echo chamber effect in various social networks. It turned out that the echo chamber effect is the result of the work of a deep neural network, which analyzes the interests and priorities of each social network user, checks other similar posts and priorities of other users, and then forms a “circle” of like-minded people for each user. Thus, the main drawback of the standard content generation algorithm is the selection of only publications and comments that support the user’s position. The goal of the study is a software architecture to solve the echo chamber problem in highly loaded social networks in real-time. The main idea was to cluster opinions on controversial topics and include user opinions from different clusters in user content. Data on controversial topics is collected from news, scholarly articles, publications, and comments with hashtags. The collected topics are clustered usi ng the K-means algorithm and the “elbow” method is used to find the optimal number of clusters. The result of the clustering of opinions by topic is provided as input data for the generation of a news feed by a recurrent neural network. The system uses Kafka as a message broker between microservices and AZURE blob storage for storing publications and comments. Both solutions support high scalability.