Secret Admirers: An Empirical Examination of Information Hiding and Contribution Dynamics in Online Crowdfunding
Bibliographic record
Abstract
Individuals’ actions in online social contexts are growing increasingly visible and traceable. Many online platforms account for this by providing users with granular control over when and how their identity or actions are made visible to peers. However, little work has sought to understand the effect that a user’s decision to conceal information might have on observing peers, who are likely to refer to that information when deciding on their own actions. We leverage a unique impression-level dataset from one of the world's largest online crowdfunding platforms, where contributors are given the option to conceal their username or contribution amount from public display, with each transaction. We demonstrate that when campaign contributors elect to conceal information, it has a negative influence on subsequent visitors’ likelihood of conversion, as well as on their average contributions, conditional on conversion. Moreover, we argue that social norms are an important driver of information concealment, providing evidence of peer influence in the decision to conceal. We discuss the implications of our results for the provision of online information hiding mechanisms, as well as the design of crowdfunding platforms and electronic markets more generally.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".