A Model for Optimally Promoting Application Diffusion on Facebook
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Facebook, the leading social networking site, has opened its platform to developers and allow them to publishapplications. Subsequently, numerous Facebook applications of various types were designed and deployed.Despite a huge portion of applications without business context, there exist a substantially increasing number ofapplications tailored specifically for marketing and advertising. From a business perspective, a Facebookapplication possesses the advantages of low development costs and strong word-of-mouth effect, which providean ideal alternative to traditional advertising formats. This paper utilizes the well-known Bass model forforecasting product diffusion, and proposes its adaptation to produce an optimal promotion budget allocation forFacebook applications. For a given application promotional budget to be used over a fixed timeframe, the modeloffers a unique solution for allocating the funds between direct promotion and indirect promotion(word-of-mouth) in order to achieve a maximum percentage of user installations from the target population ofpotential users. Numerical examples are provided to illustrate the optimal solution and suggestions are made forfuture research necessary to validate the model for possible use by practitioners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it