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 prin-cipal 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 media 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 litera-ture on brand communities and content popularity in social media. The proposed computational visual analysis methodology may inform the study of other image-based IS.
