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

arXiv: https://arxiv.org/abs/2410.04552 · HTML: https://mlsanity.com/paper/2410.04552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://mlsanity.com/paper/1711.07601/) (2017)
- [Me, My Echo Chamber, and I: Introspection on Social Media Polarization](https://mlsanity.com/paper/1803.01731/) (2018)
- [Measuring Recommender System Effects with Simulated Users](https://mlsanity.com/paper/2101.04526/) (2021)
- [Do Social Explanations Work? Studying and Modeling the Effects of Social Explanations in Recommender Systems](https://mlsanity.com/paper/1304.3405/) (2013)
- [ContentWise Impressions: An Industrial Dataset with Impressions Included](https://mlsanity.com/paper/2008.01212/) (2020)
- [Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion](https://mlsanity.com/paper/2102.04211/) (2021)

## Abstract (arXiv)

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