The Mobilizing Power of Visual Media Across Stages of Social-Mediated Protests

ABSTRACT

The popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored. For this study, we analyzed approximately ten million tweets from Twitter for three social-mediated protests (Black Lives Matter, Stop Asian Hate, and Women’s March). We found that posts with images and videos generally attracted more audience engagement than their textual counterparts. Unpacking the role of visual media across different modalities and stages of social-mediated protests, we found that the superior effects of visuals were generally more pronounced during the ignition phase of the protest than the periods before and after. By applying unsupervised image clustering on millions of protest visuals, we systematically established four common visual content categories: crowd-based protest photos, non-crowd-protest human photos, non-human photos, and non-photograph visuals. We revealed heterogeneous effects on audience engagement across content categories and protests, and explored these categories through qualitative analysis of most-engaged visuals. These findings enrich our understanding of the mobilizing power of visual media in social movements and shed light on effective communication strategies regarding social inequalities.

KEYWORDS:

• Visual media
• social-mediated protest
• social movement
• social media
• computer vision
• audience engagement
• Twitter
• temporality
• mobilization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The number of visuals is larger than the number of posts as more than 304 thousand tweets contained either multiple images or multiple videos. As we retrospectively retrieved account information and encountered a sudden shift in the Twitter Academic API, which resulted in our inability to retrieve all users’ metadata, we included posts with successfully downloaded account information in our analysis.

2. We calculated the post engagement by adding up the post likes, comments, and retweets and then normalized by the number of followers of the poster.

Additional information

Notes on contributors

Yingdan Lu

Yingdan Lu (PhD, Stanford University) is an Assistant Professor of Communication Studies at Northwestern University. Her research focuses on digital technology, political communication, and information manipulation in authoritarian and democratic contexts. Her work has appeared in peer-reviewed journals such as Political Communication, New Media & Society, Human-Computer Interaction, Computational Communication Research, and among other peer-reviewed journals. For more information, see her website: https://yingdanlu.com/

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 scholarship is at the intersection of computational social science, visual communication, science communication, and social media. His research uses computer vision methods to investigate the production and effects of visual messages across different communication contexts.