Forecasting Behavior of Social Objects

This peer-reviewed journal article introduces an innovative methodology for forecasting the popularity dynamics of content in social networks under the influence of external factors, leveraging a recurrent neural network (RNN) model. The research provides a robust framework for predicting repost trends, enabling accurate assessment of content popularity growth or decline in real time. The proposed approach advances predictive analytics for social media platforms and offers valuable applications in marketing strategy optimization, audience engagement, trend forecasting, and digital influence analysis. Published in the Herald of Khmelnytskyi National University. Technical Sciences, ISSN 2307-5732, Issue 5 (2024). DOI: 10.31891/2307-5732-2024-341-5-46