![]() It introduces Athec, a Python library developed for computational aesthetic analysis in social science research, which can be readily applied by future researchers. This article addresses this gap and provides a tutorial for social scientists to measure a broad range of hand-crafted aesthetic attributes of visual media, such as colorfulness and visual complexity. ![]() Visual aesthetics are related to a broad range of communication and psychological outcomes, yet the tools of computational aesthetic analysis are not widely available in the community of social science scholars. HCI researchers, app creators and Google Play (or another mobile marketplace) will benefit from the paper insights on what antecedes app success and how to measure the antecedents. Not only does such result assert the link between icon properties and app popularity, it also highlights the automatic prediction of app popularity as a promising research direction. The measures explained 38% of variance in the number of ratings, if app genre was accounted for. We then computationally measured two of the qualities visual saliency and complexity for 930 icons and linked the computed scores to app popularity (the number of app ratings and installs). We reviewed the visual qualities of icons that could make them noticeable and likable. App icons uniquely represent each app in Google Play and help apps to get noticed, as we demonstrate in the paper. The vast majority of the apps regardless of how well-made they are go unnoticed. ![]() The user quickly skims through the list and picks a few apps for a closer look. Almost any search on Google Play returns numerous app suggestions.
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