Mining Social Behavioral Biometrics in Twitter
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
Online Social Networking Sites (SNSs) are considered as one of the well-established mediums of mass communication in today's world. Similar to physical world humans tend to have unique pattern of social communication in virtual world. However, analysis of such web based communication patterns is rarely seen for person identification. Most of the existing biometric recognition systems use either individual physiological or behavioural traits. A framework for the analysis of the web-based social interaction data as biometric features is largely unexplored until now. In this paper, a framework to accumulate and analyze social communication based data from online SNSs is presented. Analysis of such features explores personal characteristics, knowledge, and communication patterns that can successfully be utilized as Social Behavioral Biometric features. Experimental results demonstrate that the proposed social behavioral biometric features are significantly useful for person authentication.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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