Social media fashion influencer eWOM communications: understanding the trajectory of sustainable fashion conversations on YouTube fashion haul videos
Bibliographic record
Abstract
Purpose This study aims to examine YouTube comments relevant to sustainable fashion posted on fashion haul videos over the past decade (2011–2021). It is guided by two research questions: (1) How have sustainable fashion-related comments posted on YouTube fashion haul videos changed over time? and (2) What themes are relevant to sustainable fashion in the comments posted on fashion haul videos? Design/methodology/approach A data set of comments from 110 fashion haul videos posted on YouTube was refined to only include comments with keywords related to sustainable fashion. Leximancer , a machine learning technique, was employed to identify concepts within the data and co-occurrences between concepts. Linguistic Inquiry and Word Count software was employed to assess the prevalence of concepts and identify sentiment over time. Findings Over the decade, the authors identified increased comments and conversations relevant to sustainable fashion. For instance, conversations surrounding sustainable fashion were linked to “waste” and “addicted” between 2011 and 2013, which evolved to include “environment” and “clothes” between 2014 and 2016, to “buy” and “workers” between 2017 and 2019 and “sustainable” between 2020 and 2021, demonstrating the changes in conversation topics over time. Practical implications With increasing engagement from YouTube viewers on sustainable fashion, retail-affiliated content that promotes sustainable fashion is proposed as one approach to engage viewers and promote sustainable practices in the fashion industry, whereby content creators can partner with retailers to feature products and educate viewers on the benefits of sustainable fashion. Originality/value The findings suggest that consumers are becoming more aware of and responsive to sustainable fashion. The originality of this research stems from identifying the source of this interest.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".