Pup-ularity contest: The advertising practices of popular animal influencers on Instagram
Bibliographic record
Abstract
Given the rise of online influencers, this research aims to analyze non-human social media influencers. Using a qualitative in-depth content analysis of Instagram images and captions, we analyze the social presence strategies—affective, interactive, and cohesive strategies—and the social media influencer advertising practices of top animal influencers. While all three social presence strategies were used, animal influencers most often employed affective and cohesive strategies. The results suggest that emotion and community are more important than interaction for animal influencers. Most of the influencers engaged in social media advertising by sharing brand-related posts, yet their social media feeds do not overly feature advertising messages. As an implication of the research, the research shows how animal influencers grow their parasocial relationships with followers in a way that is beneficial to brands. Thus the research reveals the role that digital animal influencers play in the social customer journey for the brands that work with them.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".