A SYSTEMATIC REVIEW OF VIRTUALHUMANS.ORG AND ITS ROLE IN VIRTUAL INFLUENCER RESEARCH, 2019 TO PRESENT
Bibliographic record
Abstract
In this paper, I conduct a systematic review of the use of VirtualHumans.org in academic studies of the virtual influencer industry. To do this, I analyze all references to and use of content from the Virtual Humans website in relevant published research from 2019 to 2024 (n=189), including the website’s database of existing virtual influencers, interviews with virtual influencer creators and virtual influencer characters themselves, articles on the state of the virtual influencer industry, and profiles of each listed virtual influencer. I pair this analysis with a brief history of VirtualHumans.org from a political economy perspective, noting the factors which went into the website’s creation; its acquisition by Offbeat Media Group, a digital marketing agency; and its organizational shifts following the sudden departure of its founder in 2023. Ultimately, I question whether academic viewpoints of the emergent virtual influencer industry, many which refer to the Virtual Humans website as a valuable resource for grasping a sense of the size and scope of the virtual influencer phenomenon, adequately consider the rooted biases and commercial interests represented by the website as not only a database but a powerful broker within the industry. Moreover, by narrating the organizational development of VirtualHumans.org as an enterprise, I contribute detailed context into the formation of these biases and commercial interests which inform its position in the virtual influencer scene.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".