Identification of the Original Author of a Social Media Post Based on Text Analysis, Time Dependencies, and the Structure of Reposts Using Combined Neural Networks

Identification of the Original Author of a Social Media Post Based on Text Analysis, Time Dependencies, and the Structure of Reposts Using Combined Neural Networks

연구 분야: Artificial Intelligence

학회: 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

This article examines the task of identifying the original author of a post on social networks by analyzing the textual content and structure of reposts. A mathematical model was proposed that describes the relationship between the original posts and their reposts, and an algorithm based on a combination of recurrent neural networks (RNN), convolutional neural networks (CNN) and graph neural networks (GNN) was presented. To train and evaluate the model, a synthetic dataset was created containing 50,000 records generated using the Faker library. The developed model has shown high accuracy (RNN 1.000, CNN - 0.9180, GNN - 0.9080) in identifying authors and can be used to analyze the dissemination of information on social networks, as well as to counter misinformation.