Verifying Online User Identity using Stylometric Analysis for Short Messages
Bibliographic record
Abstract
Stylometry consists of the analysis of linguistic styles and writing characteristics of the authors for identification, characterization, or verification purposes. In this paper, we investigate authorship verification for the purpose of user authentication process. In this setting, authentication consists of comparing sample writing of an individual against the model or profile associated with the identity claimed by that individual at login time (i.e. 1-to-1 identity matching). In addition, the authentication process must be done in a short period of time, which means analyzing short messages. Although a significant amount of literature has been produced showing high accuracy rates for long documents, it is still challenging to identify accurately authors of short unstructured documents, in particular when dealing with large authors populations. In this paper, we pose some steps toward achieving that goal by proposing a supervised learning technique combined with n-grams analysis for authorship verification for short texts. We introduce a new n-gram metric and study several sizes of n-grams using a relatively large dataset. The experimental evaluation shows increased effectiveness of our approach compared to the existing approaches published in the literature.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".