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

  • Yilang Peng University of Georgia
Keywords: computational aesthetics, computer vision, aesthetic analysis, image feature, visual complexity, authenticity


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 social science community. In this article, I address this gap and provide a tutorial on measuring hand-crafted aesthetic attributes, such as colorfulness and visual complexity. I introduce Athec, a Python library for computational aesthetic analysis in social science research. Furthermore, a case study applies Athec to compare the visual aesthetics of Instagram posts from the two candidates in the 2016 U.S. presidential election, Hillary Clinton and Donald Trump, indicating how amateurishness and authenticity are reflected in politicians’ visual messages. With computational aesthetic analysis tools, communication researchers can better understand the antecedents and outcomes of visual aesthetics beyond visual media content.

How to Cite
Peng, Y. (2022). Athec. Computational Communication Research, 4(1), 323-349. Retrieved from