Reconstructing Profiles from Information Disseminated on the Internet
Bibliographic record
Abstract
In this paper, we warn social network users about the threat that their profiles can easily be rebuilt from information disseminated on the Internet. The micro-blogging site Twitter is supposed to retain less personal information than sites like Facebook. Despite this, is it possible to reconstruct Twitter user profiles solely from publicly available information? We propose a new system based on a method of re-identification, which consists in two phases. Starting from a given Twitter user profile (first database), we try in the first phase to find his or her information scattered on other websites, such as blogs or social networks, from which we obtain a second database. The second phase of re-identification consists in forming a link between the two databases in order to reconstruct the digital identity of the user. We based our experiment on 250 randomly selected Twitter profiles on which we attempted to identify their owners. We believe that we have managed to recognize 41.6% of our sample. We conclude that the digital identity of users can be easily reconstructed solely from publicly available data that they or others have freely made available on the Internet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.011 | 0.023 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".