MoneyMouth: A Machine Learning Analysis of Altruistic Crowdfunding Success on GlobalGiving.org

<a href="https://embed.stanford.edu/iframe/?url=https%3A%2F%2Fpurl.stanford.edu%2Fpm973nb1133" class="su-underline">Show Content</a>

Abstract/Contents

Abstract: 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.

Type of resource	text
Publication date	December 8, 2023; June 6, 2023

Subject	Machine learning
Subject	Deep learning (Machine learning)
Subject	Altruism
Subject	Social service
Subject	Statistics
Subject	Social sciences > Statistical methods > Computer programs
Subject	Communication
Subject	Crowd funding
Genre	Text
Genre	Thesis

DOI	https://doi.org/10.25740/pm973nb1133
Location	https://purl.stanford.edu/pm973nb1133

Use and reproduction: User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
License: This work is licensed under a Creative Commons Zero v1.0 Universal license (CC0).

Preferred citation: Adib-Azpeitia, D. (2023). MoneyMouth: A Machine Learning Analysis of Altruistic Crowdfunding Success on GlobalGiving.org. Stanford Digital Repository. Available at https://purl.stanford.edu/pm973nb1133. https://doi.org/10.25740/pm973nb1133.

Master's Theses, Symbolic Systems Program, Stanford University

Loading usage metrics...