“And Today’s Top Donator is”: How Live Streamers on <i>Twitch.tv</i> Monetize and Gamify Their Broadcasts
Bibliographic record
Abstract
This article examines cultural and economic behavior on live streaming platform Twitch.tv, and the monetization of live streamers’ content production. Twitch is approximately the thirtieth most-viewed website in the world, with over 150 million spectators, and 2 million individuals around the world regularly broadcasting. Although less well-known than Facebook or Twitter, these figures demonstrate that Twitch has become a central part of the platformized Internet. We explore a seven-part typology of monetization extant on Twitch: subscribing, donating and “cheering,” advertising, sponsorships, competitions and targets, unpredictable rewards for viewers, and the implementation of games into streaming channels themselves. We explore each technique in turn, considering how streamers use the affordances of the platform to earn income, and invent their own methods and techniques to further drive monetization. In doing so, we look to consider the particular kinds of governance and infrastructure manifested on Twitch. By governance, we mean how the rules, norms, and regulations of Twitch influence and shape the cultural content both produced and consumed within its virtual borders; and by infrastructure, we mean how the particular technical affordances of the platform, and many other elements besides, structure how content production on Twitch might be made profitable, and therefore decide what content is made, and how, and when. Examining Twitch will thus advance our understanding of the platformization of amateur content production; methodologically, we draw on over 100 interviews with successful live streamers, and extensive ethnographic data from live events and online Twitch broadcasts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".