Predicting social media engagement with computer vision: An examination of food marketing on Instagram

Matthew Philp, Jenna Jacobson and Ethan Pancer

Journal of Business Research, 2022, vol. 149, issue C, 736-747

Abstract: In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Instagrammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’ Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to social media engagement. Results demonstrate that food images that are more confidently evaluated by Google Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow-up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and food industry trends, the more typical a food appears, the more social media engagement it receives. Using Google Vision AI to identify what product offerings receive engagement presents an accessible method for marketers to understand their industry and inform their social media marketing strategies.

Keywords: Social media marketing; Consumer engagement; Machine learning; Food; Processing fluency; Google Vision AI (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:149:y:2022:i:c:p:736-747

DOI: 10.1016/j.jbusres.2022.05.078

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