The Influence, Selection, and Management Strategies of AI Empowered Platform KOLs: A Case Study of Xiaohongshu
Bibliographic record
Abstract
In the era of self-media, the influence of Key Opinion Leaders (KOLs) on social platforms is growing and becoming a key driver of marketing. Compared to the traditional media era, KOL marketing demonstrates greater interactivity and precision. This article takes the Xiaohongshu platform as an example. Firstly, the differences in the current situation and characteristics of KOL marketing between the traditional media era and the current era are introduced, followed by an exploration of how AI technology can empower the influence of platform KOLs. It also analyses the important role of different levels of KOLs in AI technology-enabled management strategies. Artificial Intelligence (AI) technology, utilizing refined content creation optimisation, personalised audience targeting, enhanced effective interaction mechanisms, real-time data monitoring and analysis, and improved operational management efficiency. It builds a more efficient and precise marketing ecosystem for KOL groups. By analysing, this article believes that AI technology has a significant impact on enhancing the influence and commercial value of KOLs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".