The Research on the Audiences’ Psychological Under the Influence of Live Streaming of Stars on Douyin Platform ---Take Jia Nailiang as an Example
Bibliographic record
Abstract
Under the influence of the post-epidemic era, due to the limitations of people's mobility and the e-commerce mechanism that encouraged by both Internet and Douyin platform. These make the online shopping format become more popular which not only attract more people to join the e-commerce industry of Douyin platform but also attract many superstars. As superstars have their own star effect, when they are an e-commerce anchor, they will be very different from other e-commerce bloggers in attracting audiences. This article takes video blogger and star Nailiang Jia as an example which through case analysis and in-depth interviews and using Douyin's official platform Douchacha to analyze the differences between stars and anchors. According to the list of the corresponding advantages and disadvantages, which can explore the psychological activities of the audience under the influence of live streaming on the Douyin platform. The final research results show that most of the audience will place an order based on sufficient demand. Because of the failed shopping experience of some consumers, they will not place too many orders due to the star effect. In addition, the commodity explanations will affect audience psychology and the sales of products brought by anchors or stars. The authors hope that this study can provide some insights for future scholars in this field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".