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Recent studies have analyzed the factors that contribute to variations in the success of crowdfunding campaigns for a specific cancer type; however, little is known about the influential factors among crowdfunding campaigns for multiple cancers.
The purpose of this study was to examine the relationship between project features and the success of cancer crowdfunding campaigns and to determine whether text features affect campaign success for various cancers.
Using cancer-related crowdfunding projects on the GoFundMe website, we transformed textual descriptions from the campaigns into structured data using natural language processing techniques. Next, we used penalized logistic regression and correlation analyses to examine the influence of project and text features on fundraising project outcomes. Finally, we examined the influence of campaign description sentiment on crowdfunding success using Linguistic Inquiry and Word Count software.
Campaigns were significantly more likely to be successful if they featured a lower target amount (Goal amount,
Difficult-to-treat cancers and high-mortality cancers tend to trigger empathy from potential donors, which increases the funds raised. Gender differences were observed in the effects of emotional words in the text on the amount of funds raised. For cancers that typically occur in women, links between emotional words used and the amount of funds raised were weaker than for cancers typically occurring among men.
Crowdfunding allows individuals to obtain donations by raising large amounts of money through social media and other web-based services [
Crowdfunding platforms received 34.4 billion by 2015 for all campaigns [
Medical crowdfunding accounts for a large percentage of crowdfunded campaigns in the United States for several reasons: neither insurance policies nor social welfare programs fully cover people’s medical costs [
Recent studies have analyzed the factors that contribute to the variations in the success of medical crowdfunding campaigns, such as the age and gender of target audiences, the race of project founders, and the characteristics of campaign images [
Many scholars have focused on crowdfunding as the main type of medical crowdfunding. Previous studies have focused on 3 topics: First, the medical conditions underlying cancer crowdfunding play a role in funding success. Studies have shown that 65.4% of cancer crowdfunding patients were in the advanced stages of cancer [
Although several studies have focused on specific cancer types, such as thyroid [
We scraped 156,551 medical crowdfunding campaigns for cancer on the GoFundMe website from January 1, 2019, to July 11, 2021. Our data set was assembled in several steps. First, we retained only crowdfunding campaigns with English-language campaign descriptions (including countries where English is not the official language). Next, we established a cancer lexicon based on cancer types defined by the American Society of Clinical Oncology [
Summary of different cancer types.
ID | Cancer name | Counts |
1 | Bile duct | 1070 |
2 | Bladder cancer | 1825 |
3 | Bone cancer | 4407 |
4 | Brain tumor | 9651 |
5 | Breast cancer | 24,465 |
6 | Cervical cancer | 2616 |
7 | Colorectal cancer | 1180 |
8 | Esophageal cancer | 1362 |
9 | Kidney cancer | 3216 |
10 | Leukemia | 5629 |
11 | Liver cancer | 5515 |
12 | Lung cancer | 9148 |
13 | Lymphoma | 6201 |
14 | Melanoma | 1503 |
15 | Multiple myeloma | 1242 |
16 | Ovarian, Fallopian tube, and peritoneal cancer | 3256 |
17 | Pancreatic cancer | 4191 |
18 | Prostate cancer | 2425 |
19 | Stomach cancer | 2261 |
20 | Thyroid cancer | 1590 |
Website features definition.
ID | Primitive features | Definition |
1 | ID | The unique number of the project |
2 | Raised funds | Funds already raised by the project as of the crawl date (US $) |
3 | Goal amount (ln) | The target amount of funds raised by the project (US $). Because the range of values of the “Goal amount” is much larger than the other variables, logarithmic transformation was done. |
4 | Campaign type | Binary variable to indicate whether the project was launched by an individual or institution (Institutions=0, individuals=1) |
5 | Donation counts | The number of times the project has accepted donations (Different donations from the same person are counted multiple times) |
6 | Zip code | Postal code of the area where the project is initiated to get information about the geographic location of the project |
7 | HasUpdate | The binary variable indicates whether the item has been updated since it was launched (Yes=1, No=0) |
8 | HasComment | The binary variable indicates whether the item has been reviewed since it was launched (Yes=1, No=0) |
9 | Created date | Creation time of the project |
10 | Title | Title of the project |
11 | Description | Detailed description text of the project |
To facilitate the experimental analysis, we first transformed all text into lower case and removed irrelevant tokens, such as numbers, symbols, and stop words. Next, we performed word segmentation, including unigrams, bigrams, and trigrams [
Derived features definition.
ID | Inferred features | Definition |
1 | Percent funded | Represents the amount of funds raised divided by the Goal amount, (ie, what proportion of the funding goal had already been collected) |
2 | Funding outcome | Binary variable to indicate whether the project is successful or not (successful=1; unsuccessful=0)a |
3 | Duration | Duration of the projectb |
4 | Length of campaign description | Number of words in the project’s detailed description |
5 | Length of campaign title | Number of words in the project title |
6 | Number of spelling errors | Determine the number of words with spelling errors in the project details by using Python |
aGiven the positive and negative sample ratios and the large number of sponsors who failed to reach their project goals, raising a large portion of their goal money could still considerably alleviate their financial difficulties. We defined campaigns with more than 70% funding as successful. The final number of failed campaigns was 69,208, and the number of successful campaigns was 23,545.
bThe duration of the project was obtained by subtracting the time of the day the data were crawled from the time the project started. Some recently released campaigns have not yet raised funds because of their short release times. This may have interfered with experimental results. Combined with the usual 30-40 day cycle of medical crowdfunding campaigns, the campaigns within the last 40 days were filtered out.
We denote features with >2 multicategorical variables as dummy variables. For example, if an unordered multicategory variable such as cancer type is directly assigned 1, 2, 3, 4, and so forth, it has a mathematically sequential relationship from smallest to largest, which is not in line with reality. Therefore, we created a dummy variable for each category.
The independent variables we used were Funds raised to date,
We defined
Descriptive statistics for study variables.
Variables | Values | |||||
|
Mean (SD) | Minimum | 25% | 50% | 75% | Maximum |
Funding outcome | 0.199 (0.399) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Campaign type | 0.978 (0.146) | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Donation count | 55.015 (76.114) | 2.000 | 10.000 | 27.000 | 66.000 | 582.000 |
Length of description | 141.947 (102.578) | 1.000 | 70.000 | 113.000 | 183.000 | 660.000 |
Length of title | 5.023 (1.951) | 1.000 | 4.000 | 5.000 | 6.000 | 14.000 |
Goal amount (ln) | 9.007 (1.382) | 0.000 | 8.517 | 9.210 | 9.903 | 20.030 |
HasUpdate | 0.229 (0.419) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
HasComment | 0.388 (0.4872) | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 |
Duration | 16.007 (8.695) | 1.367 | 8.400 | 16.467 | 23.667 | 30.733 |
Number of spelling mistakes | 10.549 (9.531) | 0.000 | 4.000 | 8.000 | 14.000 | 60.000 |
Bile duct | 0.011 (0.105) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Bladder cancer | 0.020 (0.138) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Bone cancer | 0.047 (0.211) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Brain tumor | 0.101 (0.302) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Breast cancer | 0.266 (0.441) | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 |
Cervical cancer | 0.028 (0.166) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Colorectal cancer | 0.013 (0.112) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Esophageal cancer | 0.015 (0.12) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Kidney cancer | 0.035 (0.183) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Leukemia | 0.060 (0.238) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Liver cancer | 0.060 (0.237) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Lung cancer | 0.100 (0.299) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Lymphoma | 0.067 (0.25) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Melanoma | 0.016 (0.126) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Multiple myeloma | 0.013 (0.114) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Ovarian | 0.035 (0.184) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Pancreatic cancer | 0.045 (0.208) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Prostate cancer | 0.026 (0.16) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Stomach cancer | 0.024 (0.154) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Thyroid cancer | 0.017 (0.13) | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Pearson correlation matrix.
ID | Variable | Value | ||||||||
|
|
2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
1 | Funding outcome | –0.205 ( |
0.302 ( |
–0.133 ( |
–0.026 ( |
–0.267 ( |
0.109 ( |
0.172 ( |
–0.032 ( |
–0.122 ( |
2 | Campaign type | 1.00 | 0.046 ( |
0.044 ( |
–0.036 ( |
0.194 ( |
0.058 ( |
0.064 ( |
–0.010 ( |
0.013 ( |
3 | Donation count | —a | 1.00 | 0.280 ( |
0.036 ( |
0.403 ( |
0.295 ( |
0.527 ( |
–0.107 ( |
0.313 ( |
4 | Length of description | — | — | 1.00 | 0.141 ( |
0.272 ( |
0.114 ( |
0.142 ( |
0.007 ( |
0.751 ( |
5 | Length of title | — | — | — | 1.00 | 0.066 ( |
0.033 ( |
–0.004 ( |
–0.095 ( |
0.100 ( |
6 | Goal amount (ln) | — | — | — | — | 1.00 | 0.158 ( |
0.235 ( |
–0.078 ( |
0.232 ( |
7 | HasUpdate | — | — | — | — | — | 1.00 | 0.432 ( |
–0.089 ( |
0.110 ( |
8 | HasComment | — | — | — | — | — | — | 1.00 | –0.025 ( |
0.150 ( |
9 | Duration | — | — | — | — | — | — | — | 1.00 | –0.020 ( |
10 | Number of spelling mistakes | — | — | — | — | — | — | — | — | 1.00 |
aNot applicable.
We used penalized logistic regression (PLR) based on the
The use of L1 regularity is also known as lasso regression. Feature selection can be realized by limiting the sum of the absolute values of the estimates, such that some coefficients are equal to zero. First, because of the large number and collinearity of English phrases in our sample, we used lasso regression to retain important phrase features and set the other relevant features to zero. The use of L2 regularity is otherwise referred to as ridge regression. This type of regression is similar to lasso regression in terms of partially reducing model complexity; however, the number of features does not change. Ridge regression does not cause the coefficients to equal zero but instead only minimizes them. This outcome is not conducive to feature reduction because the model remains complex when dealing with number of features. These aspects can compromise the model performance. Therefore, we adopted lasso regression to make the model more parsimonious.
Second, overfitting is a common problem in model prediction; model parameters sometimes fit the training data too closely and hinder the general prediction ability of an overall data set. We used 10-fold cross-validation to address the covariance in our data and to prevent overfitting. Finally, because a single campaign description in the data set could only contain a mere fraction of possible English phrases, a large text sparsity matrix was obtained. Lasso regression is better suited for managing sparse data.
Because the range of values of the goal amount is much larger than that of the other variables, it will adversely affect the model convergence. Therefore, logarithmic processing was performed to narrow the range of the values. The variables were then normalized. We first added 39 control variables to the baseline model and then incorporated English phrases into the model to observe changes in the model’s explanatory power. As shown in
Summary of different models fits.
|
Model 1 | Model_2 | Model 3_count | Model 4_glove | Model 5_tf-idf |
Deviance | 105,093.11 | 80,151.81 | 62,268.85 | 67,641.44 | 62,494.18 |
Degrees of freedom | 92,752 | 92,807 | 97,037 | 93,101 | 97,132 |
CVa error (%) | 26.38 | 19.57 | 15.56 | 14.54 | 15.61 |
aCV: cross-validation.
The results section is divided into 3 thematic subsections: general campaign features, type of cancer, and text features.
In summary, features related to funding have a strong connection to crowdfunding success. As depicted in
In contrast to fundraising targets, project duration was negatively associated with success.
In the analysis, we could not limit project timeframes because GoFundMe does not set deadlines for projects, so donations can continue after a campaign goal has been reached. Fundraising projects typically spanned 30-40 days; if organizers did not make timely fundraising progress early in their campaigns, the amount raised tended not to change substantially thereafter, even though donations continued to arrive after the first surge.
Features with influential effect on success of campaign based on penalized logistic regression.
ID | Name | Coefficient | SE | ||
1 | Intercept | −1.781 | −138.414 | 0.013 | <.001 |
2 | Campaign type (organization vs individual) | −0.281 | −20.978 | 0.011 | <.001 |
3 | Donation count | 1.404 | 77.935 | 0.016 | <.001 |
4 | Length of description | −0.044 | −1.833 | 0.020 | .32 |
5 | Length of title | −0.016 | −1.212 | 0.011 | .03 |
6 | Goal amount (ln) | −1.949 | −82.767 | 0.020 | <.001 |
7 | PageHasUpdates | 0.283 | 21.175 | 0.012 | <.001 |
8 | PageHasComments | 0.671 | 43.279 | 0.013 | <.001 |
9 | Duration of campaign | −0.112 | −8.383 | 0.011 | <.001 |
10 | Number of spelling errors | −1.068 | −38.79 | 0.073 | <.001 |
11 | Bile duct | 0.100 | 8.037 | 0.012 | <.001 |
12 | Bladder cancer | 0.000 | −0.020 | 0.015 | .98 |
13 | Bone cancer | −0.023 | −1.178 | 0.019 | .24 |
14 | Brain tumor | −0.003 | −0.125 | 0.025 | .90 |
15 | Breast cancer | −0.011 | −0.303 | 0.035 | .76 |
16 | Cervical cancer | −0.049 | −2.938 | 0.017 | .003 |
17 | Colorectal cancer | 0.010 | 0.715 | 0.014 | .48 |
18 | Esophageal cancer | 0.040 | 2.986 | 0.013 | .003 |
19 | Kidney cancer | −0.022 | −1.224 | 0.018 | .22 |
20 | Leukemia | −0.012 | −0.566 | 0.021 | .57 |
21 | Liver cancer | −0.014 | −0.666 | 0.021 | .51 |
22 | Lung cancer | −0.070 | −2.755 | 0.025 | .006 |
23 | Lymphoma | 0.044 | 2.051 | 0.022 | .04 |
24 | Melanoma | 0.025 | 1.809 | 0.014 | .07 |
25 | Multiple myeloma | 0.026 | 1.868 | 0.014 | .06 |
26 | Ovarian | −0.003 | −0.199 | 0.017 | .84 |
27 | Pancreatic cancer | 0.052 | 2.803 | 0.019 | .005 |
28 | Prostate cancer | −0.008 | −0.512 | 0.016 | .61 |
29 | Stomach cancer | −0.004 | −0.228 | 0.016 | .82 |
30 | Thyroid cancer | N/Aa | N/A | N/A | N/A |
aN/A: not applicable.
Percent funded and success rate by fundraising the target amount range.
As shown in
Average percent funded and success rates by cancer type.
The project text was divided into 2 parts: the project title and a detailed description.
The relationship between project description length and fundraising results is illustrated in
Fundraising results in different title length intervals.
Length interval (%) | Funds current to goal (%) | Success ratio (%) |
1-4 words (22.96) | 41.67 | 24.78 |
4-7 words (53.74) | 41.86 | 24.83 |
7-10 words (22.13) | 40.45 | 23.26 |
10-14 words (1.17) | 36.19 | 20.75 |
Average percent funded and success rates by length of the project description.
We extracted the top 50 phrases most associated with project success or failure based on the logistic regressions that had the top 50 highest or lowest
Among the phrases most related to project failure, many were found to relate to women (eg, “grandmother,” “aunt,” “mother,” and “she”). Medical phrases associated with project failure included terms such as “ductal,” and “cervical cancer.” Thus, women seem to be at a disadvantage compared to men in terms of raising money via crowdfunding. Structurally, most phrases were unigrams and bigrams; that is, they tended to be simpler and conveyed less semantic information. Finally, compared with words common in successful campaigns, failed campaigns featured more pessimistic words such as “failure,” “cannot,” and “negative.”
Top 50 phrases signaling that the project will be funded.
Rank | Phrases |
|
1 | Hair | 2.32551 |
2 | in_aid_of | 1.873002 |
3 | thank_you | 1.834425 |
4 | Rare | 1.827049 |
5 | need_your_help | 1.628172 |
6 | the_surgeon | 1.57181 |
7 | trip | 1.513046 |
8 | lucky | 1.498507 |
9 | charity | 1.459997 |
10 | pray | 1.365733 |
11 | guy | 1.35132 |
12 | dog | 1.330811 |
13 | thyroid | 1.329916 |
14 | gift | 1.273839 |
15 | please_pray_for | 1.263614 |
16 | cancer_treatments | 1.260775 |
17 | teacher | 1.234377 |
18 | social_media | 1.220046 |
19 | staff | 1.211656 |
20 | june | 1.196617 |
21 | medical_center | 1.172205 |
22 | and_thank_you | 1.169583 |
23 | pancreatic_cancer | 1.16822 |
24 | bit | 1.166253 |
25 | radiation | 1.161074 |
26 | money_in | 1.1278221 |
27 | houston | 1.1093524 |
28 | cancer_research | 1.1046241 |
29 | her_son | 1.1004744 |
30 | you_know | 1.086452 |
31 | remove_the_tumor | 1.0801651 |
32 | meals | 1.0658478 |
33 | more_information_about | 1.0629098 |
34 | covid | 1.0564797 |
35 | and_thank_you | 1.0365421 |
36 | miles | 1.0346791 |
37 | for_weeks | 1.0330821 |
38 | college | 1.0254821 |
39 | hospice | 1.0169092 |
40 | term | 1.0140427 |
41 | friends | 1.0130013 |
42 | drug | 1.0063572 |
43 | the_lord | 1.0060987 |
44 | summer | 0.9905393 |
45 | to_recover | 0.986438 |
46 | any_way | 0.984336 |
47 | health_insurance | 0.9731374 |
48 | year_old_son | 0.9702704 |
49 | the_world | 0.9631651 |
50 | Goal | 0.9533642 |
Funding failure phrases.
Rank | Phrases |
|
1 | Ductal | –3.12579 |
2 | cervical cancer | –3.06354 |
3 | Had | –3.0504 |
4 | Grandmother | –3.03949 |
5 | Ductal | –2.396 |
6 | Weight | –2.35016 |
7 | Save | –2.23608 |
8 | UK | –2.15523 |
9 | Healing | –1.84889 |
10 | Give | –1.81071 |
11 | Daughter | –1.76682 |
12 | Grandfather | –1.74575 |
13 | Conditions | –1.73582 |
14 | and from | –1.65317 |
15 | Aunt | –1.64155 |
16 | Hospitals | –1.56082 |
17 | Mother | –1.55073 |
18 | Progress | –1.53249 |
19 | Problems | –1.51119 |
20 | Life | –1.47464 |
21 | Leg | –1.4316 |
22 | Still | –1.33643 |
23 | those who | –1.3125 |
24 | University | –1.28943 |
25 | Failure | –1.27174 |
26 | Eating | –1.26668 |
27 | can not | –1.26453 |
28 | She | –1.21934 |
29 | high school | –1.21249 |
30 | Dream | –1.21205 |
31 | Account | –1.20582 |
32 | Medications | –1.20352 |
33 | Health | –1.17978 |
34 | disease and | –1.16989 |
35 | Society | –1.15781 |
36 | of the | –1.15107 |
37 | World | –1.13135 |
38 | Alive | –1.10527 |
39 | Little | –1.10163 |
40 | Dr | –1.08841 |
41 | told that | –1.08782 |
42 | Afford | –1.0869 |
43 | Count | –1.07107 |
44 | Advised | –1.06985 |
45 | Bills | –1.06203 |
46 | living in | –1.04171 |
47 | Father | –1.03768 |
48 | a donation | –1.03597 |
49 | Breathing | –1.03458 |
50 | Negative | –1.02679 |
The social and psychological tone or sentiment of a GoFundMe campaign may also impact its success. Research suggests that the Linguistic Inquiry and Word Count (LIWC) software is ideal for identifying emotions in language [
In
For step 1, we divided the 20 cancer categories into quartiles based on the success rate of the category and focused on the top 25% (5/20) (bile duct cancer, brain tumor, lymphoma, pancreatic cancer, and melanoma) and the bottom 25% (5/20) (thyroid cancer, bladder cancer, kidney cancer, cervical cancer, and lung cancer). The proportion of positive words used for the top 25% (5/20) of cancers was higher than for the bottom 25% (5/20); that is, campaigns in the most successful cancer campaign categories were more likely to use positive words. The bottom 25% (5/20) of cancers barely used words related to expressing one’s feelings. Focusing on certainty- and risk-related words, we found that the top 25% (5/20) of cancers with the highest average campaign success rates rarely used words related to certainty, whereas nearly one-third of the bottom 25% (5/20) of cancers did so. These words were negatively related to project success; that is, the presence of words such as “really” and “actually” in project descriptions were related to lower success rates. Campaigns with low success rates mentioned risk-related words (including danger and risk) more often and indicated a painful atmosphere, which partly reduced readers’ willingness to contribute.
Step 2, focusing on the cancers with the lowest 25% and highest 25% success rates, we conducted a correlation analysis between the number of successful campaigns and 5-year survival rate. The results are presented in
In step 3, we analyzed the word sentiments of gender-linked cancers. The regression results revealed that the use of positive words in the description was positively related to crowdfunding success in breast cancer (
Words related to friendship, such as
In the larger category of cognitive words, health-related words such as
The use of words related to money (including
Linguistic Inquiry and Word Count and binary logistic regression.
|
Female cancers | Male cancers | ||||||
|
Cancers that most often affect women | Cancers that most often affect men | ||||||
|
Breast cancer | Ovarian | Cervical cancer | Prostate cancer | Colorectal cancer | Lung cancer | ||
Success number | 6216 | 802 | 544 | 646 | 314 | 1699 | ||
Success rate, % | 25.40 | 24.60 | 20.80 | 26.60 | 26.60 | 18.60 | ||
|
||||||||
|
Positive emotion | 0.093 ( |
0.157 ( |
0.068 ( |
0.080 ( |
0.239 ( |
0.077 ( |
|
|
Negative emotion | −0.167 ( |
−0.325 ( |
−0.154 ( |
−0.250 ( |
−0.551 ( |
−0.158 ( |
|
|
Anxiety words | 0.093 ( |
0.253 ( |
0.199 ( |
0.336 ( |
0.501 ( |
0.177 ( |
|
|
Anger words | 0.249 ( |
0.441 ( |
0.342 ( |
0.104 ( |
0.145 ( |
0.323 ( |
|
|
Sadness words | 0.067 ( |
0.328 ( |
0.226 ( |
0.254 ( |
0.312 ( |
0.295 ( |
|
|
||||||||
|
Friend words | 0.062 ( |
−0.083 ( |
0.138 ( |
−0.152 ( |
0.167 ( |
0.155 ( |
|
|
Female references | −0.052 ( |
−0.038 ( |
−0.028 ( |
−0.180 ( |
−0.035 ( |
−0.028 ( |
|
|
||||||||
|
Insight words | 0.053 ( |
0.117 ( |
0.052 ( |
0.095 ( |
0.071 ( |
0.063 ( |
|
|
Discrepancy words | −0.084 ( |
−0.115 ( |
−0.069 ( |
−0.150 ( |
−0.156 ( |
−0.099 ( |
|
|
Tentative words | −0.017 ( |
−0.032 ( |
−0.125 ( |
−0.143 ( |
−0.054 ( |
−0.066 ( |
|
|
Certainty words | 0.000 ( |
0.027 ( |
−0.080 ( |
−0.102 ( |
0.06 ( |
0.037 ( |
|
|
Differentiation | −0.086 ( |
−0.085 ( |
−0.049 ( |
−0.121 ( |
−0.003 ( |
−0.075 ( |
|
|
||||||||
|
See words | 0.059 ( |
0.033 ( |
0.091 ( |
−0.200 ( |
0.148 ( |
0.027 ( |
|
|
Feel words | −0.046 ( |
0.150 ( |
0.115 ( |
−0.039 ( |
−0.039 ( |
−0.002 ( |
|
|
||||||||
|
Health words | −0.072 ( |
−0.046 ( |
−0.069 ( |
−0.084 ( |
−0.067 ( |
−0.069 ( |
|
|
Ingestion words | 0.110 ( |
−0.064 ( |
0.076 ( |
−0.065 ( |
−0.058 ( |
−0.125 ( |
|
|
||||||||
|
Reward words | 0.115 ( |
0.168 ( |
0.012 ( |
0.207 ( |
0.333 ( |
0.285 ( |
|
|
Risk words | −0.075 ( |
−0.192 ( |
−0.009 ( |
0.032 ( |
0.159 ( |
−0.122 ( |
|
|
||||||||
|
Work words | −0.001 ( |
0.061 ( |
−0.005 ( |
0.020 ( |
−0.017 ( |
0.043 ( |
|
|
Leisure words | 0.143 ( |
0.124 ( |
0.358 ( |
0.229 ( |
−0.037 ( |
0.118 ( |
|
|
Money words | −0.065 ( |
−0.063 ( |
−0.119 ( |
−0.059 ( |
−0.126 ( |
−0.097 ( |
|
|
Religion words | −0.309 ( |
−0.376 ( |
−0.353 ( |
−0.314 ( |
−0.183 ( |
−0.232 ( |
Correlation between funding success number and 5-year survival rate (the top 25% and the bottom 25%).
|
Success number (top 25%) | 5-year survival rate (top 25%) | Success number (bottom 25%) | 5-year survival rate (bottom 25%) | |
|
|||||
|
Pearson correlation | 1 | −0.131 | N/Aa | N/A |
|
Significance (2-tailed test) | N/A | 0.833 | N/A | N/A |
|
Case number | 5 | 5 | N/A | N/A |
|
|||||
|
Pearson correlation | −0.131 | 1 | N/A | N/A |
|
Significance (2-tailed test) | 0.833 | N/A | N/A | N/A |
|
Case number | 5 | 5 | N/A | N/A |
|
|||||
|
Pearson correlation | N/A | N/A | 1 | −0.945b |
|
Significance(2-tailed test | N/A | N/A | N/A | 0.015 |
|
Case number | N/A | N/A | 5 | 5 |
|
|||||
|
Pearson correlation | N/A | N/A | −0.945b | 1 |
|
Significance (2-tailed test) | N/A | N/A | 0.015 | N/A |
|
Case number | N/A | N/A | 5 | 5 |
aN/A: not applicable.
bCorrelation is significant at the 0.05 level (2-tailed).
The purpose of this project was to investigate the relationship between GoFundMe cancer campaign features and the success of these campaigns and to understand the impact of text features on campaign success for various cancers. The results suggest that there are numerous interrelated factors that may contribute to the success of a cancer crowdfunding campaign and they can be categorized as campaign features and text features.
The number of donations to date, frequency of updates, sponsor of the fundraiser being a charity organization rather than an individual, and type of cancer were important contributing factors to the success of the fundraising project. On the basis of the number of donations, we specifically noted a Matthew effect, in that “For to all those who have, more will be given” [
Specifically, the more donations a project receives, the more people are drawn to donating money. Consistent with previous studies, people appeared to trust a project after viewing an expansive donation record, which may motivate potential donors to donate. Simultaneously, web-based medical crowdfunding campaigns usually receive a number of small donations; the higher the number of donors, the more likely a project is to meet its goal [
Campaigns by nonprofit organizations or institutions were more likely to achieve fundraising success than individual campaigns. This is consistent with other researchers’ findings, which show that organizations usually have greater social influence and credibility than individual organizers, thereby attracting more attention and trust [
Campaign updates posted by organizers significantly and positively affect a project’s success, and genuine comments from others can boost a project’s credibility and attract potential donors [
Organizers who emphasize the rarity of a beneficiary’s cancer type are more likely to receive donations. With GoFundMe, users are less likely to feel empathy when reading projects devoted to common types of cancer [
We speculate that crowdfunding success for cancers is not entirely contingent on the project description but is rather related to factors such as the 5-year survival rate, cure difficulty, and prevalence of the target cancer. Although lung cancer remains one of the deadliest cancers, developments in medicine have gradually reduced mortality rates for common cancers, such as cervical cancer and lung cancer, and this may be reflected in poorer fundraising outcomes. For example, estimates from the American Cancer Society have shown that the incidence and mortality associated with late-stage lung cancer have declined significantly in recent years [
Top 10 cancers by rates of cancer deaths all types of cancer, United States, 2014 to 2018 and top 10 cancers by average percent funded.
Top 10 cancers by rates of cancer deaths | Age-standardized (rate)a | Top 10 cancers by percent funded |
Lung and bronchus | 38.5 | Bile duct |
Female breast | 20.1 | Pancreatic cancer |
Prostate | 19 | Lymphoma |
Colon and rectum | 13.7 | Brain tumor |
Pancreas | 11 | Esophageal cancer |
Liver and intrahepatic bile duct | 6.6 | Melanoma |
Ovary | 6.3 | Prostate cancer |
Leukemias | 5.4 | Leukemia |
Non-Hodgkin lymphoma | 4.4 | Breast cancer |
Corpus and uterus, NOSb | 4.3 | Colorectal cancer |
aRate per 100,000 people Source: US Cancer Statistics Working Group. US Cancer Statistics Data Visualizations Tool, based on 2020 submission data (1999-2018): US Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute [
bNOS: not otherwise specified.
Women are typically thought to possess an advantage in the donation process [
Five-year survival rate and gender distribution for selected cancers.
Cancer type | 5-year survival rate (%) | Gender distributiona | |
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Bile duct | 19 | 0 |
|
Brain tumor | 35 | 0 |
|
Lymphoma | 87 | 0 |
|
Pancreatic cancer | 9 | 0 |
|
Melanoma | 92 | 0 |
|
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Thyroid cancer | 98 | 1 |
|
Bladder cancer | 77 | 0 |
|
Kidney cancer | 75 | 0 |
|
Cervical cancer | 66 | 1 |
|
Lung cancer | 19 | 0 |
aFemale cancers were coded as 1 and male cancers were coded as 0.
Both longer project titles and descriptions, offering more detailed information, stimulated readers’ willingness to donate. Social psychology research suggests that increasing the number of arguments on a page can increase its persuasive impact power [
Text sentiment is an important factor in campaign success. Texts that contain positive emotions are more advantageous than those that contain negative emotions. Donors are more likely to be motivated by positive emotions, for example, “thank you,” “need your help,” and “please pray for.” These words imply an optimistic attitude of the fundraiser despite the illness and resonate with donors. Evidently, positive words create a more positive mood in readers; these fundraisers generate greater empathy among readers when browsing and underscore the value of donors’ contributions. In contrast, descriptions of beneficiaries’ current plight, expressing negative emotions (eg, fear, discontent, and resentment), or negative words (eg, negative terms and anxiety-related terms, tentative words, and risk-related words) in the project description were associated with lower levels of fundraising success. Thus, recommendations for campaign organizers are to display more positive emotions and use declarative words (eg, positive emotions; words related to certainty and leisure) to create a relaxed and optimistic atmosphere. Carefully crafted descriptions can inspire readers’ sympathy and ultimately increase the likelihood of donation.
More detailed emotional categories obtained through LIWC highlighted the varying influences of different emotional expressions on the success ratio. Campaigns with more positive emotion words, friend-related words, and leisure words were more likely to raise money. These words suggest more positive life attitudes, stronger social connections, and a more hopeful outlook on an individual’s future, generating greater empathy among readers and underscoring the value of donors’ contributions. In addition, project descriptions displaying concrete attitudes and determination (ie, certainty words) were more likely to be related to campaign success than text featuring uncertain words (ie, tentative words).
Finally, disparities were observed in the effects of emotional words on gender-specific cancer projects. Campaigns for male-linked cancers were more likely to be affected by the emotion generated from sadness words and anxiety words. Because of the more difficult cure and lower 5-year survival rates for cancers prevalent among men, the painful negative emotions portrayed in these words were more likely to garner sympathy from donors. Therefore, the emotions of helplessness and grief displayed in the descriptions of male cancer projects are more likely to result in effective fundraising.
Although crowdfunding websites such as GoFundMe offer an opportunity to observe a wide range of thoughts and behaviors, our data have some limitations. First, people who earn a greater income and are fully insured might not need money from donations for cancer treatment. Therefore, they may not have created campaigns on crowdfunding websites. Second, the data included only US-based samples. It is possible that individuals in countries with universal health systems (eg, Canada) may not experience the same financial burden for cancer treatments. They may also describe their medical campaigns in a different manner than people in the United States, leading to different predictors of campaign success. In addition, we did not analyze the cultural background of the individuals requesting funds through crowdfunding nor did we assess donors’ cultural backgrounds. Cultural differences within the United States and between the United States and non-Western countries may limit the generalizability of these findings to donors’ perceptions and behaviors outside specific groups in the United States. Third, without a controlled experiment, we were unable to draw a causal relationship between campaign characteristics and fundraising outcomes. Nevertheless, our analyses identify likely candidate predictors of fundraising success in the United States. Finally, similar to the study by Silver et al [
Despite these limitations, to the best of our knowledge, our study is the first cross-sectional study to provide large-scale quantitative support for the notion that difficult-to-treat cancers and high-mortality cancers receive more funds than common cancers and to analyze gender differences in the effects of emotional words in the text on the amount of funds raised.
Four areas for future research arise in this study. First, future studies are needed to replicate this analysis in other countries, especially those with different cultural norms and medical systems, to identify macrolevel influences on crowdfunding success. Second, future researchers could use more direct data collection methods to gather founders’ and donors’ demographic and socioeconomic data. With these data, more robust models to control for unobserved characteristics that may be correlated with crowdfunding success, such as the founder’s socioeconomic status, may help gain clues about additional drivers of crowdfunding success. Third, further studies should explore donation behavior using experimental settings to provide stronger evidence for a causal relationship between the characteristics of the funders/campaign descriptions and the donations they receive. Finally, future researchers could analyze the effects of visual and textual fusion on crowdfunding success.
The effectiveness of medical crowdfunding for patients with cancer depends on a variety of interlocking factors. The most salient features are campaign features and campaign textual features. First, the type of cancer plays a role in funding success with rare cancers and cancers with lower survival rates (eg, bile duct cancer), easily outranking more common and less lethal cancers (eg, lung cancer). Second, men are more likely to receive higher funding rates than women. Indeed, a man with prostate cancer can raise up to 5.3 times more money than a woman with breast cancer.
However, the strongest predictor of success appears to be the goal amount (moderate vs too high). Setting a reasonable and not-too-high fundraising goal will greatly contribute to the success of a crowdfunding campaign. This is followed by the number of donations already made and the existence of comments from friends or donors.
Finally, the textual features that increase the likelihood of reaching one’s fundraising goal include moderately long texts, positive affect words, friend-related and leisure words (for select cancer types), words about cognitive insights, and visual sensory words, but, surprisingly, anger words and anxiety words for some cancers. In contrast, words that reduce the likelihood of campaign success include words about discrepancies, differentiation, words referring to women, health words, words related to risk, money, and religion, and negative emotion words. Taken together, the results of this study suggest that an upbeat campaign with many comments and early donations that projects optimism and personal insights without emphasizing religious or gender-related themes may be most likely to be successful.
cross-validation
Global Vectors for Word Representation
Linguistic Inquiry and Word Count
penalized logistic regression
term frequency–inverse document frequency
None declared.