Looking at Google and Samsung through an Orientalist and Cultural Lens: A Multimodal and Thematic Analysis of Advertisement Videos on YouTube
Bibliographic record
Abstract
This major research paper (MRP) explores similarities and differences, cultural distinctions, and stereotypes present within technology advertising. In this MRP, I examine a selection of YouTube videos made by Samsung and Google for their North American (America and Canada) and South Asian (Pakistan and India) channels. With a focus on Google’s Year in Search 2020 campaign and Samsung’s Galaxy S22 campaign, this study seeks to answer the following questions: (1) How are Google and Samsung’s North American and South Asian advertising campaigns similar and different from one another? (2) Do both companies’ Indian and Pakistani advertising campaigns reinforce or challenge Orientalist stereotypes? (3) Is there an emphasis on individualism in the North American advertising campaigns and an emphasis on collectivism in the South Asian advertising campaigns? To answer these inquires, I conducted a qualitative content analysis which involved coding the visual, textual and audio elements in Google and Samsung’s YouTube videos in order to identify similarities, differences and themes. The results suggest that Google’s North American and South Asian videos were similar, and Samsung’s American, Canadian and Pakistani videos also had many similarities. However, Google and Samsung’s advertising videos for India were significantly distinct and different from the other channels’ videos. The findings indicated that Orientalist stereotypes were largely being reinforced by both companies, with a few instances of stereotypes being challenged. Finally, Google’s Year in Search 2020 videos placed more of an emphasis on collectivism than individualism; Samsung’s Galaxy S22 North American videos were more individualistic while the South Asian videos mainly emphasized collectivism. This study is relevant for professional communicators since it highlights the ongoing use of Orientalist stereotypes, categorizations, and arguments in tech advertising and provides a framework for identifying and critically engaging with Orientalism in promotional videos and other multimodal products.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".