How Do You Earn Money on Live Streaming Platforms?—A Study of Donation-Based Markets
Bibliographic record
Abstract
Donation-based markets have been implemented by many online platforms, such as live streaming platforms. In these markets, producers provide services without mandatory charges, and customers enjoy the services and voluntarily donate money to the producers. The donation is split between the producers and platform with a pre-agreed fraction. To gain insights into the market operation, we use a two-stage game to capture the sequential decision process between the platform and producers. In Stage I, the platform decides a donation-split-fraction (DSF), i.e., the fraction of donation kept by the producers. In Stage II, producers decide whether to participate in the platform and (if yes) how to choose their service attributes considering the DSF as well as the producers' and customers' preferences. We prove that the Stage II game is a potential game with a counter-intuitive equilibrium result: although a larger DSF leads to more producer participation and a better match between the producers' choices and the customers' preferences, it does not necessarily lead to more total donation. The Stage I problem, nevertheless, is challenging to solve analytically due to its non-convexity. To gain insights regarding the optimal DSF that maximizes the platform's payoff, we characterize both its upper-bound and lower-bound. We show numerically that the platform's optimal payoff always decreases with the mismatch between the producers' and customers' preferences. Finally, we conduct a case study with the dataset from Twitch and demonstrate the approach of computing the platform's optimal DSF without the producers' inherent preferences.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".