社会化媒体
产品(数学)
客户参与度
广告
社交媒体营销
营销
用户参与度
代理(统计)
情感(语言学)
过程(计算)
业务
心理学
计算机科学
万维网
几何学
数学
沟通
机器学习
操作系统
作者
Matthew Philp,Jenna Jacobson,Ethan Pancer
标识
DOI:10.1016/j.jbusres.2022.05.078
摘要
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.
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