![Sample our Communication Studies journals, sign in here to start your access, 2013 & 2014 volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5927/TOC-Subject-Sample-2021-Information-Science2.jpg"})
ABSTRACT
To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opportunities of automated visual analysis for the study of social media effects and present an overview of existing computational methods that can facilitate this. Specifically, we highlight the gap between the outputs of existing computer vision tools and the theoretical concepts relevant to media effects research. We propose multiple approaches to bridging this gap in automated visual analysis, such as justifying the theoretical significance of specific visual features in existing tools, developing supervised learning models to measure a visual attribute of interest, and applying unsupervised learning to discover meaningful visual themes and categories. We conclude with a discussion about future directions for automated visual analysis in computational communication research, such as the development of benchmark datasets designed to reflect more theoretically meaningful concepts and the incorporation of large language models and multimodal channels to extract insights.
Acknowledgement
The authors wish to thank the editors of the special issue for their feedback and guidance. The authors also thank Andreu Casas, Zhengjie Miao, and Sibo Jia for their comments on earlier versions of the article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Yilang Peng
Yilang Peng (PhD, Annenberg School for Communication, University of Pennsylvania) is an Assistant Professor in the Department of Financial Planning, Housing and Consumer Economics at the University of Georgia. His research areas include computational social science, visual communication, artificial intelligence, social media, and science communication.
Irina Lock
Irina Lock (PhD, USI Università della Svizzera italiana) is a Professor of Strategic Communication at the Institute of Communication Science, Friedrich Schiller University Jena. She analyses digital communication phenomena in strategic and political communication and studies the role of visuals.
Albert Ali Salah
Albert Ali Salah (Ph.D., Boğaziçi University) is a Professor and Chair of Social and Affective Computing at the Department of Information and Computing Sciences, Utrecht University. His research is on automatic analysis of human behaviour, computational social science, multimodal interaction, and affective computing.