Influencer profiling: a comprehensive categorisation of social media influencers and their association with digital engagement
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
Influencer marketing has evolved significantly, becoming more sophisticated and integral to brand success. Addressing the limitations of previous studies, this article presents an empirically validated categorisation of influencers by simultaneously considering their personal characteristics and content attributes. Using data from over 11,000 YouTube videos, we propose an empirical categorisation of six influencer profiles – Expert, Motivator, Attractive, Productive, Perfectionist, and Middle-of-the-road – based on several personal characteristics such as trustworthiness, originality, expertise, and over 40 linguistic elements. Moreover, we demonstrate the association between these profiles and different metrics of digital consumer engagement. This research advances the literature on social media influencers, offering valuable insights for developing effective influencer marketing strategies.
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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 | 0.005 | 0.003 |
| 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 | 0.000 |
| 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