Introducing a Novel Framework for Recognizing Social Media Recommenders Under Absent Recommendations and a First Graph Neural Network-Based Implementation

Recommender algorithms shape public discourse on social media, often intensifying polarization and accelerating misinformation. Their opacity and integration within social networks make assessing their true impact exceptionally difficult. To make these systems more explainable, we introduce the concept of Social Media Recommenders Recognition under Absent Recommendations (SM-ARR), which accounts for the complexity induced by platforms not publicly releasing interaction logs and algorithmic details, which hinders detecting specific misbehaviors, e.g., dark patterns. We also present a proof-of-concept implementation, SM-ARR-G (Social Media Automatic Recommender Recognition through Graph Neural Networks), a Graph Neural Network (GNN) based framework designed to identify which recommendation algorithm is shaping interactions during a given period, without requiring access to the platform’s internal recommendation logs. SM-ARR-G learns to forecast user actions by combining their past behavior with candidate "infospheres", simulated content exposure patterns generated by alternative recommender models. The candidate that produces the most accurate generalization performance is taken as the best explanation of the recommender shaping the observed dynamics. Our evaluation draws on the DBLP-Citation-Network V14 dataset, chosen for its scale and structural richness as a proxy for social media data. While not a typical social media graph, it offers many of the same relational patterns, dense connectivity, influence dynamics, and recommendation-like exposure. By embedding multiple recommender algorithms into this environment, we create controlled yet varied scenarios. This enables us to assess how consistently SM-ARR-G can identify the hidden recommender across different conditions, without relying on access to proprietary platform data. Our experiments demonstrate that our SM-ARR-G approach is promising and can reliably detect the hidden recommender while highlighting how alternative algorithms alter user interaction patterns. While further improvements are necessary (e.g., handling unknown or evolving recommenders), we expect the framework to complement existing audit strategies, broaden the possibilities for recommender assessment, and improve explainability of social media dynamics.