While user-generated images represent important information sources in IS in general and in social media in particular, there is little research that analyzes image design and its effects on image popu-larity. We introduce an innovative computational approach to extract image design characteristics that includes convolutional neural network-based image classification, a dimensionality reduction via principal component analysis, manual measurement validation, and a regression analysis. An analysis of 790,775 car images from 17 brands posted in 68 car model communities on a social me-dia platform reveals several effects of product presentation on image popularity that relate to the levels of utility reference, experience reference, and visual detail. A comparison of economy cars and premium cars shows that car class moderates these image design effects. Our results contribute to the extant literature on brand communities and content popularity in social media. The proposed computational visual analysis methodology may inform the study of other image-based IS.