1 How COVID-19 Has Changed Crowdfunding: Evidence From GoFundMe Junda Wang, Xupin Zhang*, and Jiebo Luo*, Fellow, IEEE F Abstract—While the long-term effects of COVID-19 are yet to be deter- and the other is for after the COVID-19 outbreak (2020). mined, its immediate impact on crowdfunding is nonetheless significant. In this regard, we also analyze whether the influencing This study takes a computational approach to more deeply comprehend factors have changed under the influence of the pandemic. this change. Using a unique data set of all the campaigns published over Some researchers believed the emotional elements conveyed the past two years on GoFundMe, we explore the factors that have led to through text and facial expressions are likely to attract the successful funding of a crowdfunding project. In particular, we study a corpus of crowdfunded projects, analyzing cover images and other donors [2]. We extract the face from the picture and judge variables commonly present on crowdfunding sites. Furthermore, we the emotion using the Baidu Application Programming In- 1 construct a classifier and a regression model to assess the significance terface (API) . In addition, we extract and infer individual- of features based on XGBoost. In addition, we employ counterfactual level features such as gender, race, age, beauty, target, lo- analysis to investigate the causality between features and the success cation, followers, shares, distinct donors, family status, and of crowdfunding. More importantly, sentiment analysis and the paired facial attractiveness, as well as duration of crowdfunding. sample t-test are performed to examine the differences in crowdfunding Text is one of the most basic aspects of information transfer, campaigns before and after the COVID-19 outbreak that started in and some researchers believed that textual elements includ- March 2020. First, we note that there is significant racial disparity in ing descriptions, reviews, and emotion have a considerable crowdfunding success. Second, we find that sad emotion expressed through the campaign’s description became significant after the COVID- impact on the success of crowdfunding [3]. Therefore, we 19 outbreak. Considering all these factors, our findings shed light on the incorporate text emotion into models through a text scoring impact of COVID-19 on crowdfunding campaigns. model that produces the scores as a feature. According to the characteristics mentioned above, a comprehensive and Index Terms—crowdfunding, GoFundMe, counterfactual, topic model, in-depth analysis can be performed. If a campaign page XGBoost visitor’s sympathy with specific project topics is given, a more longer visit duration would increase the probability 1 INTRODUCTION that the page visitor donates and thus the success of the crowdfunding [3]. Hence, the aesthetic and technical scores In recent years, with the development of the Internet, there of the cover image are also considered as factors affecting are more ways to raise money online. GoFundMe, an Amer- the success of crowdfunding [4]. Based on the above data, ican for-profit crowdfunding platform that encourages peo- we propose three hypotheses: ple to create online crowdfunding projects about their life events such as illnesses and accidents, is a good example. • Hypothesis 1: The basic features of crowdfunding, Even if crowdfunding becomes more and more convenient, fundraiser and descriptions have a significant impact arXiv:2106.09981v2 [cs.CY] 2 Aug 2021 the success rate of crowdfunding is still not high. To date, on fund-raising success. there is not much information regarding a recipe for a • Hypothesis 2: There are significant differences be- successful campaign, and the factors leading to the success tween before and after the COVID-19 outbreak. of crowdfunding have become a critical research goal [1]. • Hypothesis 3: There are certain social disparities In the current study, we analyze GofundMe to extract related to different races and the compound factors, the most critical factors that contribute to crowdfunding which consist of the impact of COVID-19 and other success. We collect data of 36,370 campaigns on GoFundMe social factors. and split them into two parts. One part of the crowd- funding data is for before the COVID-19 outbreak (2019), The goal of this study focuses on analyzing the main factors that affect crowdfunding campaigns and the impact • Junda Wang is with the Department of Computer Science, University of of the COVID-19 on these factors. Notably, we define a Rochester, Rochester, NY 14627. success group where the crowdfunding projects have raised E-mail: [email protected] more than the target amounts, and a failure group oth- • Xupin Zhang is with the Department of Human Development, University of Rochester, Rochester, NY 14627. erwise. Moreover, we build predictions models for both E-mail: [email protected] classification of success/failure and regression of the raised • Jiebo Luo is with the Department of Computer Science, University of amount. For regression or classification problems, we use Rochester, Rochester, NY 14627. E-mail: [email protected] 1. https://cloud.baidu.com/doc/FACE/s/Uk37c1m9b 2 XGBoost to solve these two problems and provide the list text data and obtain three predicted emotion scores of essential factors. Finally, we perform a counterfactual including positive, negative, and neutral scores, re- experiment to analyze the influences of different factors and spectively. Finally, we examine the text using LIWC the significant impact of the COVID-19 on these factors. To to obtain more detailed text sentiment scores: sad our best knowledge, this is the first study examining the scores, anger scores, and anxiety scores. However, it impact of COVID-19 on crowdfunding campaigns. is evident that both descriptions of the campaigns are essential in impacting crowdfunding success. These potential effects are not easy to measure directly, 2 RELATED WORK therefore we train an XLNet model to predict the In the past few years, more platforms have been created success of the crowdfunding campaign and learn its to provide online crowdfunding, such as Kickstarter, In- potential effects [13]. diegogo, and GoFundMe. Particularly, crowdfunding plat- • Image Features: In terms of image features, we com- forms aim to be intermediaries between sponsors and pare the DeepFace API [14] with the Baidu API, and fundraisers. They can also promote the ideas of the also consider the previous research comparing the fundraiser and call for support from potential investors [5]. Baidu API and other competing APIs [15]. In the end, In addition to the factors mentioned above, the funding goal we choose the Baidu API, which is a platform provid- [6] and project duration [7] have a considerable impact on ing reliable face recognition services. The Baidu API the success of crowdfunding. returns facial attractiveness, age, race, emotion, and Increasingly more studies have investigated crowdfund- gender of each face in the cover image (also referred ing success using data-mining methods. Specifically, they to as profile image). For simplicity, we calculate the predict crowdfunding success and analyze the factors that mean attractiveness of faces when an image contains are significant to crowdfunding. Mollick argued that it is more than one person. For the age feature, we not difficult to measure success and performed a manual anal- only calculate the mean age but also return two char- ysis to distinguish between success and failure [8]. Yuan acteristics: the number of children and the number of identified the importance of topics and sentiment further older adults. People under 15 years old are defined affecting the success of crowdfunding [9]. Notably, Mitra as children, while those over 60 years are regarded and Gilbert predicted the success of crowdfunding through as older adults. We regard the number of people analyzing text using LIWC [10]. Lauren examined the re- of different races as the characteristics variable. In lationship between the emotion of the cover image and the Baidu API, there are four different races: Black, the crowdfunding success [2] and found that crowdfunding White, Asian, and Others. In terms of gender, we platforms rely on emotions to attract sponsors. obtain the number of males and females. In the end, we extract the emotion of each face (happy, sad, 3 DATA AND FEATURES grimace, neutral, or angry). 3.1 Data sets We focus on GofundMe to analyze the crucial factors con- 4 METHODOLOGY AND FINDINGS tributing to the success. Specifically, we crawl all the avail- able 36,370 crowdfunding campaigns on GofundMe and 4.1 Campaign Category divide them into two parts. One part was collected before There are 21 different categories of campaigns and their August 2019, while the other was collected after August distribution is shown in Figure 1. Some of these categories 2020. The purpose of this division is to analyze whether are related to the success rate of crowdfunding, while others COVID-19 has had an impact on people’s attitude towards are not. Specifically, we analyze the categories that have crowdfunding or whether some influential factors or less the most significant impact on crowdfunding success using influential factors have changed. The features of the dataset the t-tests. We find that the impact of some categories on are summarized below and shown in Table 1. crowdfunding success has changed over the past two years. Since it makes no sense to compare p values directly, we 3.2 Basic Features compare whether their significance levels has changed. For Travel & Adventure The features that can be directly extracted from the web- example, the significance levels of the , Non-Profits & Charities Medical, Illness & Healing Celebrations site are as follows: launch date, cover image, description, , , & Events Creative Arts, Music & Film Accidents & Emergencies category, current amount, goal amount, # of followers, # of , , Dreams, Hops & Wishes shares, and # of donors. We refer to these features as the and categories have changed. In sum- basic features.