Gender and age in the language of social media: An easier way to build credibility
Bibliographic record
Abstract
The use of celebrity endorsements is one of the most popular strategies used by companies today. Celebrities can bring product advantages through advertising and go beyond the complexities of competitive advertising communications. The company invests a large amount of money to get the attention of consumers and gain a competitive position in the market. The purpose of this study is to explore the effect of celebrity trust on the credibility of advertisements, brands, and companies, then the influence between the credibility of advertisements, brands, and companies, and will also explore the role of gender and age as moderating variables. The study used a quantitative method, the sample was taken based on purposive sampling in Jakarta and used the artist with the most followers as the object of research who endorsed food and beverage companies. The results of this study explain that there is a significant influence between celebrity trust on all credibility, gender and age managed to moderate the influence of celebrity trust on credibility. This study provides input to managers and food and beverage companies in using endorsements on Instagram social media as their marketing strategy, especially for companies that have a market share of young people in accordance with the characteristics of the respondents in this study.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".