Bibliographic record
Abstract
Social advertising exploits the interconnectivity of users in social networks to spread advertisement and generate user engagements. A lot of research has focused on how to select the best subset of users in a social network to maximize the number of engagements or the generated revenue of the advertisement. However, there is a lack of studies that consider the advertiser's value-per-engagement, i.e., how much an advertiser is maximally willing to pay for each engagement. Prior work on social advertising is based on the classical framework of influence maximization. In this paper, we propose a model where advertisers compete in an auction mechanism for the influential users within a social network. The auction mechanism can dynamically determine payments for advertisers based on their reported values. The main problem is to find auctions which incentivize advertisers to truthfully reveal their values, and also respect each advertiser's budget constraint. To tackle this problem, we propose a new truthful auction mechanism called TSA. Compared with existing approaches on real and synthetic datasets, TSA performs significantly better in terms of generated revenue.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".