Why am I seeing this? Towards recognizing social media recommender systems with missing recommendations

• URL: http://arxiv.org/abs/2504.11000v1
• Date: Tue, 15 Apr 2025 09:16:17 GMT
• Title: Why am I seeing this? Towards recognizing social media recommender systems with missing recommendations
• Authors: Sabrina Guidotti, Sabrina Patania, Giuseppe Vizzari, Dimitri Ognibene, Gregor Donabauer, Udo Kruschwitz, Davide Taibi,
• Abstract summary: We introduce a method for Automatic Recommender Recognition using Graph Neural Networks (GNNs)<n>Our approach enables accurate detection of hidden recommenders and their influence on user behavior.<n>This study provides insights into how recommenders shape behavior, aiding efforts to reduce polarization and misinformation.
• Score: 4.242821809663174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media plays a crucial role in shaping society, often amplifying polarization and spreading misinformation. These effects stem from complex dynamics involving user interactions, individual traits, and recommender algorithms driving content selection. Recommender systems, which significantly shape the content users see and decisions they make, offer an opportunity for intervention and regulation. However, assessing their impact is challenging due to algorithmic opacity and limited data availability. To effectively model user decision-making, it is crucial to recognize the recommender system adopted by the platform. This work introduces a method for Automatic Recommender Recognition using Graph Neural Networks (GNNs), based solely on network structure and observed behavior. To infer the hidden recommender, we first train a Recommender Neutral User model (RNU) using a GNN and an adapted hindsight academic network recommender, aiming to reduce reliance on the actual recommender in the data. We then generate several Recommender Hypothesis-specific Synthetic Datasets (RHSD) by combining the RNU with different known recommenders, producing ground truths for testing. Finally, we train Recommender Hypothesis-specific User models (RHU) under various hypotheses and compare each candidate with the original used to generate the RHSD. Our approach enables accurate detection of hidden recommenders and their influence on user behavior. Unlike audit-based methods, it captures system behavior directly, without ad hoc experiments that often fail to reflect real platforms. This study provides insights into how recommenders shape behavior, aiding efforts to reduce polarization and misinformation.

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