Identifying Users from Online Interactions 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
In recent years, the mass growth of online social networks has introduced a completely new platform of analyzing human behavior. Human interactions via online social networks leave big trails of behavioral footprints, which have been investigated by many researchers for the purpose of targeted advertising and business. However, analysis of such online interactions is rarely seen for user identification. The main objective of this paper is to analyze individuals' online interactions as biometric information. In this paper, we investigated how online interactions retain behavioral characteristics of users and how consistent they are over time. For this purpose, we proposed a novel method of identifying users from online interactions in Twitter. Identification performance has been evaluated on a database of 50 Twitter users over five different time periods. We obtained very promising results from experimentation, which demonstrate the potential of online interactions in aiding the authentication process of social network users'.
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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 | 0.002 |
| Open science | 0.001 | 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