StyleLink: User Identity Linkage Across Social Media with Stylometric Representations
Bibliographic record
Abstract
User identity linkage (UIL) is the task of aligning user identities of the same user across different social network platforms. Although existing approaches have explored various aspects such as different user profile attributes and social network structures, the writing styles from user-generated texts, which is commonly known as stylometry, remain relatively underexplored. In this paper, we propose a novel Graph Neural Network (GNN)-based model named StyleLink, which leverages both social network structures and stylometric features derived from user-generated texts to address the UIL problem in an integrated manner. Our model utilizes GNNs to incorporate both stylometric features and the network structure for each social network, effectively embedding the network and enhancing user representation. This is the first work to incorporate stylometric features into GNNs to embed social networks and then conduct UIL between two embedding spaces. Extensive experiments on real-world social network datasets demonstrate the superior performance of StyleLink over existing state-of-the-art methods, achieving higher accuracy in user linkage and improved ranking of identity matches. In addition, we explore the effects of different linguistic characteristics in the identification of user identities and visualizes the effects of applying GNNs for better social network embedding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".