Persuasion in Advertising Discourse by Saudi Influencers on Snapchat: An Analytical Study Through the Lens of Speech Acts
Bibliographic record
Abstract
In the present digital age, characterized by the ubiquity of various social media platforms with diverse advertisements targeting users, this study aims to investigate the persuasion in advertising discourse by Saudi influencers on Snapchat through the lens of speech acts. Using a qualitative approach, the data was collected from 20 Snapchat influencers (10 males and 10 females) by recording their advertisements on Snapchat. Content analysis was used to analyze the data using the frameworks of Searle’s (1969) speech acts. The findings indicated that Saudi Snapchat influencers used a variety of speech acts in their advertisements to persuade their audience effectively. Specifically, assertive speech acts were predominant (54.1%), followed by directive (22.9%), expressive (15.8%), declarative (6.0%), commissive (0.7%), and quotation (0.5%) acts. The study concluded by addressing some marketing implications and offering various recommendations for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".