Humor as Monotony Breaker in Funny Ads: A Multi-Modal Discourse Analysis of Ads of Pakistani Ufone & Jazz Cellular Companies
Bibliographic record
Abstract
The present study explores and analyzes the role of humor in the advertisement as the message in advertisements keeps a pivotal value constructing and promoting the idea of promotional activity by any company. So, the companies and promoters of products utilize and employ various distinctive and discursive strategies to promote their products focusing on the interests of their consumers. The research concludes that Pakistan is the one amongst other countries, in which, advertisers and promotors utilize humor as an important feature in advertainments at a huge level. Along with entertainment, the promoters promote the text and its discourse through humor which influences the decision-buying process of consumers. However, they often try to utilize humor neutralizing the social or personal values. Recent studies prove that intelligent and sensible use of humor by the companies can lead them to success if it is more relevant to the products’ nature and function. A Multi-Modal Approach, employing Fairclough’s (1989) 3D model along with Kress and van Leeuwen’s (1996) grammar of visual design, is employed to analyze data. The findings of the study suggest that the humor if handled properly, can play a vital role in captivating consumers’ interest towards the product as it takes them away from the tensions of life and makes the product more appealing. Moreover, humor as a monotony breaker makes the ad more attractive that ultimately adds to the promotion of the product.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".