A Semisupervised Approach to Predicting a Twitch Streamer’s Growth based on the Game Streamed
Bibliographic record
Abstract
Games live streaming is growing rapidly as a form of entertainment. A game streamer will like to know what game to stream in order to attract huge number of viewers and followers which in turn will generate sizable income for the streamer. Using streamer’s metrics, the main goal of this research work is to design and develop a set of resources that a streamer can use to maximize the number of viewers and followers for a particular game and when to play the game. This research develops two models using machine learning techniques that can be used by game streamers to maximum the returns on investment. When both model predictions are presented as percentage, Model 1 using regression algorithms provides a MAE of 5.48 meaning the prediction has an error within 5.48% of the streamer’s total follower count. Also, Model 1 has 85.46% of its predictions’ absolute error less than or equal to 5. Similarly Model 2 with 2.53 MAE and 87.68% of its predictions’ absolute error less than or equal to 5.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".