Bouchaud, P., & Ramaciotti, P. (2026). Recommender system in X inadvertently profiles ideological positions of users. arXiv [cs.SI].
Summary
This paper asks whether X’s friend-recommendation system inadvertently learns and exploits users’ political orientations, even though it is not explicitly designed to profile ideology. Using a data donation program that gathered over 2.5 million “Who to Follow” recommendations from 682 French volunteers, the authors reconstruct an approximation of X’s 256-dimensional user embedding from publicly documented architectural details. They find a single spatial direction in this embedding that correlates almost perfectly (ρ=0.887) with users’ Left-Right ideological positions, independently of demographics. The paper then proposes an iterative orthogonal projection method that removes this linearly-encoded political information while largely preserving recommendation relevance, offering both a diagnostic and a candidate regulatory-compliance tool.
Key Contributions
- First empirical, quantitative measurement of inadvertent political profiling in a large-scale real-world recommender serving individual regular users.
- A reproducible methodology for approximating a closed recommender’s internal embedding from donated exposure data plus public architectural knowledge — applicable beyond X.
- Empirical demonstration of the linear representation hypothesis in a deployed AI system at scale.
- A constrained-recommendation procedure that strips sensitive linearly-encoded attributes while retaining topical relevance, framed as a possible compliance tool for GDPR, LGPD, PIPA, nFADP, and the DSA.
- A conceptual argument that AI explainability undermines the legal distinction between active and passive profiling.
Methods
The authors ran a browser plug-in data donation campaign collecting 2,549,008 WTF recommendations from 682 volunteers (Jan 2023–May 2024), plus follower networks, profiles, and recent posts for a study population of ~26,500 users. They reconstructed X’s embedding via constrained optimization using a TransE scoring function, training a 256-dimensional space with Adagrad and a convex combination of “Follow” and “WTF” losses, using mixed negative-sampling strategies. Validation relied on AUC-ROC (0.700) and Precision@k against heuristic baselines and extensive robustness checks. User attributes (Left-Right and anti-elite position, age, gender, popularity, topic interests) were inferred using ideology scaling calibrated to the Chapel Hill Expert Survey and off-the-shelf demographic and topic classifiers. Canonical Correlation Analysis identified attribute-aligned directions, and an iterative orthogonal projection (adapting debiasing techniques from the ML fairness literature) removed the ideological direction.
Findings
- The reconstructed embedding predicted held-out recommendations well (AUC-ROC 0.700, Precision@1 0.725), far exceeding baselines.
- CCA directions correlated strongly with attributes: Left-Right (0.887), anti-elite (0.863), news interest (0.848), popularity (0.730), age (0.562), gender (0.384), all p<0.0001.
- Attribute directions were largely orthogonal; only age and news interest showed significant alignment.
- The Left-Right direction correctly ordered followers of French parties (La France Insoumise leftmost through Rassemblement National rightmost).
- The Left-Right encoding was not reducible to demographics (Spearman ρ=0.172 with age, −0.275 with gender).
- Inference was robust to variation in training parameters, negative sampling, simulated device-coverage bias, demographic sub-sampling, temporal splits, and friend-graph evolution.
- Removing the Left-Right direction increased ideological diversity of recommendations (Cohen’s d=0.477) while preserving topical relevance (topic-distribution cosine similarity=0.948); it produced the largest change of any attribute removed.
Connections
This paper is by one of the authors of the closely related audit work Bouchaud2026-np, and shares that project’s focus on reconstructing and interrogating platform recommender behavior under the platform-governance and data-access agenda. It extends recommender-audit literature that previously emphasized item-level diversity or the causal effects of algorithmic policies toward analyzing the internal user representations these systems learn. The remaining papers under these topics deal largely with news exposure, polarization, and platform data access at the content level rather than embedding-level profiling, so the intellectual overlap beyond the shared methodological lineage is limited.
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