Research Area:  Machine Learning

Abstract:

The increasing volume of images published online in a wide variety of contexts requires communication researchers to address this reality by analyzing visual content at a large scale. Ongoing advances in computer vision to automatically detect objects, concepts, and features in images provide a promising opportunity for communication research. We propose a research protocol for Automated Visual Content Analysis (AVCA) to enable large-scale content analysis of images. It offers inductive and deductive ways to use commercial pre-trained models for theory building in communication science. Using the example of corporations’ website images on sustainability, we show in a step-by-step fashion how to classify a large sample (N = 21,876) of images with unsupervised and supervised machine learning, as well as custom models. The possibilities and pitfalls of these approaches are discussed, ethical issues are addressed, and application examples for future communication research are detailed.

Keywords:  
communication
Automated Visual Content Analysis
unsupervised
supervised
machine learning

Author(s) Name:  Theo Araujo, Irina Lock, Bob van de Velde

Publisher name:  Taylor & Francis

DOI:  10.1080/19312458.2020.1810648

Volume Information:  Volume 14

Paper Link: https://www.tandfonline.com/doi/full/10.1080/19312458.2020.1810648