Temporal Pattern in Tweeting Behavior for Persons' Identity Verification
Bibliographic record
Abstract
Social interactions via Online Social Network (OSN) can provide a gamut of information about users that have been recently studied as behavioral patterns for person recognition. Similar to social interactions, the temporal information of persons in OSN is likely to exhibit behavioral characteristic and habitual pattern. This paper presents the first empirical study to answer a question whether temporal information obtained via OSN may contain sufficient behavioral biometric properties. In this paper, we present a methodology to identify a set of idiosyncratic temporal features and develop a system based on those unique features for identity verification. To the best of our knowledge, this is the first study on identity verification based on solely temporal profile obtained from an online social network. Experiments demonstrate that the proposed unique temporal profile in OSN can be utilized for users' identity verification, as it obtained low EER of 12% and high AUC of 95.2% in a closed-set test scenario. Potential applications of the proposed temporal profile include identity verification, anomaly and fraud detection, identity theft, continuous authentication, human behavior analysis, and so on.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".