Athec: A Python Library for Computational Aesthetic Analysis of Visual Media in Social Science Research

Abstract

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. 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. It introduces Athec, a Python library developed for computational aesthetic analysis in social science research, which can be readily applied by future researchers. In addition, a case study applies Athec to compare the visual aesthetics of Instagram posts from the two candidates in the 2016 US presidential election, Hillary Clinton and Donald Trump, showing how amateurishness and authenticity are reflected in politicians’ visual messages. With tools of computational aesthetic analysis, communication researchers can better understand the antecedents and outcomes of visual aesthetics beyond the content of visual media.

Keywords: computational aesthetics, computer vision, aesthetic analysis, image feature, visual complexity, authenticity