How Medical Crowdfunding Helps People? A Large-scale Case Study on Waterdrop Fundraising

TL;DR

This study analyzes over 27,000 online medical crowdfunding cases to understand success factors, highlighting the importance of social networks, profile credibility, and machine learning predictions in medical crowdfunding outcomes.

Contribution

It provides the first large-scale empirical analysis of medical crowdfunding success factors and demonstrates the predictive power of machine learning models in this context.

Findings

Social network spread significantly influences fundraising success.

Profile credibility, including geographic details, affects donation amounts.

Machine learning models can predict fundraising success effectively.

Abstract

While online medical crowdfunding achieved tremendous success, quantitative study about whether and how medical crowdfunding helps people remains little explored. In this paper, we empirically study how online medical crowdfunding helps people using more than 27, 000 fundraising cases in Waterdrop Fundraising, one of the most popular online medical crowdfunding platforms in China. We find that the amount of money obtained by fundraisers is broadly distributed, i.e., a majority of lowly donated cases coexist with a handful of very successful cases. We further investigate the factors that potentially correlate with the success of medical fundraising cases. Profile information of fundraising cases, e.g., geographic information of fundraisers, affects the donated amounts, since detailed description may increase the credibility of a fundraising case. One prominent finding lies in the effect of social network on the success of fundraising cases: the spread of fundraising information along social network is a key factor of fundraising success, and the social capital of fundraisers play an important role in fundraising. Finally, we conduct prediction of donations using machine learning models, verifying the effect of potential factors to the success of medical crowdfunding. Altogether, this work presents a data-driven view of medical fundraising on the web and opens a door to understanding medical crowdfunding.