How social media users trust advertising more than you think
Bibliographic record
Abstract
Consumers today are no longer bound to advertisers in their search for product information. Social media platforms and technologies are now empowering consumers to share product information with each other. This act is disempowering marketers and their agencies of record that are struggling to be relevant in the age of Web 2.0. Despite the low switching costs of turning away from traditional advertising to obtain product information, unscrupulous marketing practices persist. We maintain that these actions are feeding a rising mistrust in commercial advertising that is encouraging even more consumers to engage in electronic word-of-mouth (eWOM) on social media platforms. This paper looks at this rising mistrust and how it might describe consumers who engage in electronic word-of-mouth about products after purchase. We rely on Web survey data from the Canadian Tourism Commission (CTC) which looks at a population of UK consumers that purchase travel and then go on to describe their trip to others using social media. Unique to this study is the use of social values methodology to measure trust in advertising. Our findings suggest that social media users trust advertising in spite of current advertising practices, while those who do not use social media, score below the mean on this trend. Theoretically, our results imply that trust in advertising may have more to do with a consumer’s fundamental outlook on life than it does with the credibility of the source where the advertising itself came from. More practically, our results provide a measure of certainty for company executives who may wish to use social media as part of more integrated marketing campaigns designed to capture user-generated content and to redirect this highly influential source of marketplace information at other consumers.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".