Bibliographic record
Abstract
Douyin is a short video-sharing mobile app that is similar to famous social media platforms like Instagram and Tiktok. The paper highlights the key features of Douyin, and its effect on short-video marketing in the field of literature and research. The paper will introduce the development of short video marketing, discuss the impact of Douyin on short video market, and provide suggestions for companies to better utilize Douyin. Moreover, the paper will highlight the development trends of Douyin and its impact on short video marketing in the future. The research method applied in the paper was focused on the four channels of sales promotion, individual behavior, content creation and influencer endorsement. The survey included companies such as Instagram, Twitter, and Snapchat. Document analysis included the specific promotion channels, the promotion time frame and promotion percentage. The results of this research showed that almost three-quarters of the companies (72.2%) employed social media marketing strategies. Douyin has played a significant role in the rise of short video marketing. Douyin has opened a new channel for product promotion and brand-building activities. Douyin's rapid development has brought about a change in distribution strategies of online content. Short videos are more attractive than traditional videos because of their shortness and popularity among people. In addition, it can make product promotion more effective and convenient because companies do not have to pay for ads on TV advertisements anymore due to the high cost. Douyin can also be used as a channel for recruitment and influencer endorsements. The rapid development of Douyin has brought about changes in how people consume media.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".