Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2015 ACM Workshop on Multimodal Deception Detection, WMDD 2015. As deception behavior permeates on almost every human interaction, there is growing interest to understand and recognize the nature of deceptive behavior in multiple domains. The goal of this workshop is to provide the participants with a forum to foster the dissemination of ideas on computational and behavioral methodologies for deception detection. We are very excited about the success of the first edition of this workshop and the unique opportunity of gathering researchers from different fields to share their perspectives on deception detection. The call for papers attracted submissions from United States, Canada, and Europe, which resulted in five accepted papers that will be presented during the workshop. The program also includes three excellent invited speakers: we are grateful to Dr. Yejin Choi (University of Washington), Dr. Jeffrey Hancock (Cornell University), and Dr. Ioannis Pavlidis (University of Houston) for agreeing to speak at our workshop. We couldn't have hoped for a better slate of speakers and presentations!
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.031 |
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".