Bibliographic Details
| Title: |
MoneyMouth: A Machine Learning Analysis of Altruistic Crowdfunding Success on GlobalGiving.org |
| Authors: |
Adib-Azpeitia, Danya, Hancock, Jeffrey, Wu, Jiajun |
| Publisher Information: |
Stanford Digital Repository, 2025. |
| Publication Year: |
2025 |
| Subject Terms: |
Crowd funding, Deep learning (Machine learning), Communication, Machine learning, Statistics, FOS: Mathematics, Altruism, Social service, Social sciences--Statistical methods--Computer programs |
| Description: |
NGOs often face knowledge barriers regarding how to write effective fundraising requests on crowdfunding platforms. This issue motivates the use of mixed computational methods to identify the linguistic features associated with successful campaigns and predict whether a campaign will succeed. The research incorporates Linguistic Inquiry and Word Count (LIWC), Pearson correlation coefficients (PCC), logistic regression (LR), Bidirectional Encoder Representations from Transformers (BERT), and Local Interpretable Model-Agnostic Explanations (LIME) with the goal of unifying computer and social science. The PCCs calculated on GlobalGiving.org projects suggest that community-based thinking, storytelling, and readability are characteristics of campaigns that reach their target fundraising amount. The LR model’s most heavily-weighted linguistic features corroborate those results. The fine-tuned BERT model performed slightly better than the logistic regression at predicting a crowdfunding campaign’s success. |
| Document Type: |
Thesis |
| DOI: |
10.25740/pm973nb1133 |
| Rights: |
CC 0 |
| Accession Number: |
edsair.doi...........8f2c229cf63629c189bcce5b386422e2 |
| Database: |
OpenAIRE |