Camera eats first: exploring food aesthetics portrayed on social media using deep learning

Abstract

Purpose The purpose of this paper is to explore and examine discrepancies of food aesthetics portrayed on social media across different types of restaurants using a large-scale data set of food images. Design/methodology/approach A neural food aesthetic assessment model using computer vision and deep learning techniques is proposed, applied and evaluated on the food images data set. In addition, a set of photographic attributes drawn from food services and cognitive science research, including color, composition and figure–ground relationship attributes is implemented and compared with aesthetic scores for each food image. Findings This study finds that restaurants with different rating levels, cuisine types and chain status have different aesthetic scores. Moreover, the authors study the difference in the aesthetic scores between two groups of image posters: customers and restaurant owners, showing that the latter group tends to post more aesthetically appealing food images about the restaurant on social media than the former. Practical implications Restaurant owners may consider performing more proactive social media marketing strategies by posting high-quality food images. Likewise, social media platforms should incentivize their users to share high-quality food images. Originality/value The main contribution of this paper is to provide a novel methodological framework to assess the aesthetics of food images. Instead of relying on a multitude of standard attributes stemming from food photography, this method yields a unique one-take-all score, which is more straightforward to understand and more accessible to correlate with other target variables.

Artificial intelligence research in hospitality: a state-of-the-art review and future directions

“…Some researchers Artificial intelligence research adopted the perspectives of customers and employees to examine the performance of hospitality businesses. For instance, Gambetti and Han (2022) studied the aesthetic differences between food images posted by businesses and pictures posted by customers.…”

Applying image recognition techniques to visual information mining in hospitality and tourism

“…Using Google's Cloud Vision API, which can integrate multiple CNN-based models, Lin et al (2021) demonstrated that photos from social media and surveys share major similarities in key image characteristics. Furthermore, Gambetti and Han (2022) proposed a neural food aesthetic assessment model and found that the aesthetic scores of photos differ across restaurants and cuisine types. Knowing how to extract valuable visual information from large-scale user-generated photos to infer consumers' online behavior and its influence on firm performance is a promising research direction and crucial for business practices in hospitality.…”

“…Using Google’s Cloud Vision API, which can integrate multiple CNN-based models, Lin et al (2021) demonstrated that photos from social media and surveys share major similarities in key image characteristics. Furthermore, Gambetti and Han (2022) proposed a neural food aesthetic assessment model and found that the aesthetic scores of photos differ across restaurants and cuisine types.…”

Guest editorial: Creating, managing and marketing gastronomy experiences in hospitality and tourism

“…By developing a neural assessment model of food aesthetic combined with computer vision and deep learning techniques, Gambetti and Han (2022) assess a food images data set consisting of 50,018 Yelp food images for 577 restaurants. The methodological contribution is twofold: firstly, they develop a deep learning informed image quality assessment model that gives aesthetic scores to food images that reflect the image quality without further processing; secondly, they leverage computer vision methods to acquire a large set of low-level visual features that are indicative of aesthetic qualities.…”