Scholarlink

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’ impact is essential but challenging due to the complex, distributed nature of social media networks as well as limited access to real-world data and in particular recommendations, usually not reported. 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 (in which a recommender can play a main role) from user behavior prediction, allowing us to simulate recommender-generated infospheres and assess different recommenders’ impact on 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

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sabrina Guidotti,
  • Gregor Donabauer,
  • Simone Somazzi,
  • Udo Kruschwitz,
  • Davide Taibi,
  • Dimitri Ognibene

摘要

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’ impact is essential but challenging due to the complex, distributed nature of social media networks as well as limited access to real-world data and in particular recommendations, usually not reported. 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 (in which a recommender can play a main role) from user behavior prediction, allowing us to simulate recommender-generated infospheres and assess different recommenders’ impact on 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