# Extraction of Visual Information to Predict Crowdfunding Success

arXiv: https://arxiv.org/abs/2203.14806 · HTML: https://mlsanity.com/paper/2203.14806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 80 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [MultiGrain: a unified image embedding for classes and instances](https://mlsanity.com/paper/1902.05509/) (2019)
- [bartMachine: Machine Learning with Bayesian Additive Regression Trees](https://mlsanity.com/paper/1312.2171/) (2013)
- [Prediction with Missing Data via Bayesian Additive Regression Trees](https://mlsanity.com/paper/1306.0618/) (2013)
- [Variable selection for BART: An application to gene regulation](https://mlsanity.com/paper/1310.4887/) (2013)
- [What Contributes to a Crowdfunding Campaign's Success? Evidence and Analyses from GoFundMe Data](https://mlsanity.com/paper/2001.05446/) (2020)

## Abstract (arXiv)

Researchers have increasingly turned to crowdfunding platforms to gain insights into entrepreneurial activity and dynamics. While previous studies have explored various factors influencing crowdfunding success, such as technology, communication, and marketing strategies, the role of visual elements that can be automatically extracted from images has received less attention. This is surprising, considering that crowdfunding platforms emphasize the importance of attention-grabbing and high-resolution images, and previous research has shown that image characteristics can significantly impact product evaluations. Indeed, a comprehensive review of empirical articles (n = 202) that utilized Kickstarter data, focusing on the incorporation of visual information in their analyses. Our findings reveal that only 29.70% controlled for the number of images, and less than 12% considered any image details. In this manuscript, we review the literature on image processing and its relevance to the business domain, highlighting two types of visual variables: visual counts (number of pictures and number of videos) and image details. Building upon previous work that discussed the role of color, composition and figure-ground relationships, we introduce visual scene elements that have not yet been explored in crowdfunding, including the number of faces, the number of concepts depicted, and the ease of identifying those concepts. To demonstrate the predictive value of visual counts and image details, we analyze Kickstarter data. Our results highlight that visual count features are two of the top three predictors of success. Our results also show that simple image detail features such as color matter a lot, and our proposed measures of visual scene elements can also be useful. We supplement our article with R and Python codes that help authors extract image details (https://osf.io/ujnzp/).