Constructing Positivity in Live-stream E-commerce: An Appraisal Analysis of Interpersonal Stance in Chinese Digital Retail Live-talk
Bibliographic record
Abstract
This study investigates how positive interpersonal meanings are discursively constructed in Chinese live-stream e-commerce, examining the evaluative discourse of an influential anchor’s promotional talk through the lens of Appraisal Theory and Positive Discourse Analysis. Drawing on three highly viewed live-stream sessions by Yuhui Dong, the analysis combines manual annotation with qualitative illustration to trace the patterned distribution of Attitude resources in real-time consumer interaction. Findings reveal a systematic preference for positive stance-taking, with Affect emerging as the most salient attitudinal domain. Expressions of dis/inclination are particularly prominent, foregrounding forward-looking emotional alignment and shared optimism. Judgement resources are dominated by veracity, naturalizing sincerity, transparency, and ethical reliability as key foundations for trust-building. Appreciation mainly concentrates on valuation, positioning commodities in terms of pragmatic usefulness and embodied benefit rather than technical specification. Across these systems, positivity is not merely a matter of enthusiastic tone, but an interactional strategy calibrated to cultivate affiliation, mitigate perceived risk, and construct consumption as a socially meaningful, emotionally anchored practice. The study demonstrates that persuasive force in live-stream e-commerce rests on the orchestration of affective resonance, moral credibility, and value-centered framing. By showing how evaluative meanings accumulate across interactional phases to stabilize alignment and motivate purchase intention, the research extends the analytical reach of Positive Discourse Analysis into digital retail contexts. The findings contribute to emerging understandings of stance work in mediated marketplaces and offer discourse-based insights for fostering ethical, trust-oriented engagement in contemporary online commerce.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".