Harnessing Deep Learning for Crowdfunding Success Prediction: A Comparative Analysis on Kickstarter Dataset
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
The rise of crowdfunding has transformed the landscape of fundraising for community projects, social initiatives, micro-enterprises, and startups, utilizing internet technology to connect donors with project creators worldwide. This research aims to evaluate the effectiveness of deep learning techniques in predicting the success of reward-based crowdfunding campaigns. We specifically applied Long Short-Term Memory (LSTM) models and a hybrid Gated Recurrent Units (GRU)-LSTM model, to conduct a critical analysis of the factors influencing crowdfunding success. Our Kickstarter project dataset, incorporating textual, numerical, and categorical features, forms the basis for this analysis. Results indicate that the Bidirectional LSTM model achieved the highest accuracy at 93%, while the Encoder-Decoder LSTM and hybrid GRU-LSTM models also demonstrated strong predictive performance, with accuracies of 92% and 91%, respectively. These findings offer valuable insights that can support backers in assessing the likelihood of project success, fostering more informed funding decisions, and enhancing crowdfunding outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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