A New Model for Rating Users’ Profiles in Online Social Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Profiling users in Online Social Networks (OSNs) is of great benefit in multiple domains (e.g., marketing, sociology, and forensics). In this paper, we propose a new model for rating user’s profile (i.e., low, medium, high, and advanced) in an OSN community by embedding it into clusters located at predefined range of radius in a low-dimensional Cartesian space. The orthogonal coordinates of the profile are estimated using Principle Component Analysis (PCA) applied on a vector of metrics formulated as a set of attributes of interest (i.e., qualitative and quantitative) mined from the user’s profile to characterize his/her level of participation and behavior in the community. The experimentations are conducted on 3000 simulated profiles of three OSNs (Facebook, Twitter and Instagram) by embedding them in three cartesian spaces of three corresponding communities (Religion, Political and Lifestyle). The results show that we are able to estimate accurately the profile rates by reducing the vector of metrics to a low-dimensional space whittle down to 3-D space.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it