Corpus-Based Word Usage in Social Media Marketing and E-Commerce: Developing Word Lists and a Proposedly Designed Teaching
Bibliographic record
Abstract
This study aims to explore the frequently used words in 41 research articles and 12 related books from 2000 to 2024 in the context of social media marketing and e-commerce, focusing on general words, academic words, and specialized words. The investigation also leads to an in-depth examination of collocation words using Antconc software's N-gram feature. Then, the collected words were grouped for designing the proposed design for teaching English for students majoring in social media marketing and e-commerce. In addition, grammatical usage was analyzed to provide a comprehensive picture of frequently used grammatical structure for supporting the effectiveness of a proposedly designed teaching. The investigation focuses on a large dataset of 1,675,230 tokens and 39,931 types, with keyword tokens numbering 818,111 and keyword types numbering 1,791. The vocabulary distribution contains 21.36% K1 words, 7.89% K2 words, and 18.27% from the Academic Word List (AWL), with off-word lists accounting for 52.48%. Key findings emphasize the importance of terms such as social media marketing, e-commerce, and marketing strategies, indicating the key role of social platforms and digital media in contemporary advertising practices. Furthermore, the examination of n-grams and academic vocabulary emphasizes the value of data-driven strategies, technological innovation, and consumer behavior observations. Specialized vocabulary highlights developing trends and innovations in this industry. The findings also enhance the understanding of the growing language of social media and e-commerce marketing, providing insights into current industry trends and terminology. This analysis provides the foundation for developing targeted educational instruments and methods to improve communication and effectiveness in digital marketing environments.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".