#Bloompartner: The use of Influencer Marketing in the Growth of Health and Wellness Products
Bibliographic record
Abstract
Bloom Nutrition's Bloom Greens & Superfoods powder has been a viral commodity taking over everyone's TikTok "for you page" (FYP).TikTok has become a means for product marketing, and Bloom Nutrition has found marketing success, gaining traction on the platform, specifically through the use of macro-influencers.This thesis explores how Bloom Nutrition uses lifestyle influencers on TikTok as a channel for the promotion of Bloom Greens & Superfoods powder and explores how the product is being incorporated into the TikToks of these influencers.This research uses multimodal discourse analysis to identify how the intersecting communicative resources of visuals, content, and discourse in the TikToks of Alix Earle, Gabriela Moura, and Anastasia Karanikolaou work in conjunction to successfully market the product.I rely on the concept of the "performing self" as my theoretical framework throughout my formal analysis.The analysis suggests that Bloom Nutrition uses lifestyle influencers and their physical appearances to advertise their product while subtly pushing a health and wellness discourse through TikToks.Ultimately, the brand successfully promotes its product through the marketing strategy of product placement, incorporating the product into the regular content of viral TikTok influencers.This study contributes to the developing field of Communication Studies, exploring the prosperity of consumer culture and the commercial nature of TikTok.I am beyond grateful for the support of many people throughout my academic research and writing process.Firstly, I would like to thank my thesis advisor, Dr. Irena Knezevic, whose thoughtful advice, guidance, kindness, and unwavering support were pivotal to the creation and development of this thesis.Secondly, I would like to
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".