Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach

TL;DR

This paper uses Graph Neural Networks to model and analyze the impact of social media recommendation algorithms by leveraging academic networks as a proxy, enabling simulation and prediction of social dynamics.

Contribution

It introduces a novel GNN-based approach to simulate social media recommendation impacts using academic networks, addressing data access limitations.

Findings

Effective prediction of future co-authorships

Separation of info prediction from behavior modeling

Enhanced understanding of recommendation system roles

Abstract

The widespread use of social media has highlighted potential negative impacts on society and individuals, largely driven by recommendation algorithms that shape user behavior and social dynamics. Understanding these algorithms is essential but challenging due to the complex, distributed nature of social media networks as well as limited access to real-world data. This study proposes to use academic social networks as a proxy for investigating recommendation systems in social media. By employing Graph Neural Networks (GNNs), we develop a model that separates the prediction of academic infosphere from behavior prediction, allowing us to simulate recommender-generated infospheres and assess the model's performance in predicting future co-authorships. Our approach aims to improve our understanding of recommendation systems' roles and social networks modeling. To support the reproducibility of our work we publicly make available our implementations: https://github.com/DimNeuroLab/academic_network_project