Dissertation/ Thesis
MoneyMouth: A Machine Learning Analysis of Altruistic Crowdfunding Success on GlobalGiving.org
| Τίτλος: | MoneyMouth: A Machine Learning Analysis of Altruistic Crowdfunding Success on GlobalGiving.org |
|---|---|
| Συγγραφείς: | Adib-Azpeitia, Danya, Hancock, Jeffrey, Wu, Jiajun |
| Στοιχεία εκδότη: | Stanford Digital Repository, 2025. |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | Crowd funding, Deep learning (Machine learning), Communication, Machine learning, Statistics, FOS: Mathematics, Altruism, Social service, Social sciences--Statistical methods--Computer programs |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | Thesis |
| DOI: | 10.25740/pm973nb1133 |
| Rights: | CC 0 |
| Αριθμός Καταχώρησης: | edsair.doi...........8f2c229cf63629c189bcce5b386422e2 |
| Βάση Δεδομένων: | OpenAIRE |
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