Harnessing Deep Learning for Crowdfunding Success Prediction: A Comparative Analysis on Kickstarter Dataset
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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".