Temporal Pattern in Tweeting Behavior for Persons' Identity Verification
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
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.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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