Food Choice Mimicry on a Large University Campus

• URL: http://arxiv.org/abs/2308.16095v1
• Date: Wed, 30 Aug 2023 15:44:01 GMT
• Title: Food Choice Mimicry on a Large University Campus
• Authors: Kristina Gligoric, Arnaud Chiolero, Emre K{\i}c{\i}man, Ryen W. White, Eric Horvitz, Robert West
• Abstract summary: We study social influence on food choice through carefully designed causal analyses on a major university campus. We find strong evidence of a specific behavioral mechanism for how dietary similarities arise: purchasing mimicry. The results of this study elucidate the behavioral mechanism of purchasing mimicry and have further implications for understanding and improving dietary behaviors on campus.
• Score: 38.37343044833543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social influence is a strong determinant of food consumption, which in turn influences health. Although consistent observations have been made on the role of social factors in driving similarities in food consumption, much less is known about the precise governing mechanisms. We study social influence on food choice through carefully designed causal analyses, leveraging the sequential nature of shop queues on a major university campus. In particular, we consider a large number of adjacent purchases where a focal user immediately follows another user ("partner") in the checkout queue and both make a purchase. Identifying the partner's impact on the focal user, we find strong evidence of a specific behavioral mechanism for how dietary similarities between individuals arise: purchasing mimicry, a phenomenon where the focal user copies the partner's purchases. For instance, across food additions purchased during lunchtime together with a meal, we find that the focal user is significantly more likely to purchase the food item when the partner buys the item, v.s. when the partner does not, increasing the purchasing probability by 14% in absolute terms, or by 83% in relative terms. The effect is observed across all food types, but largest for condiments, and smallest for soft drinks. We find that no such effect is observed when a focal user is compared to a random (rather than directly preceding) partner. Furthermore, purchasing mimicry is present across age, gender, and status subpopulations, but strongest for students and the youngest persons. Finally, we find a dose-response relationship whereby mimicry decreases as proximity in the purchasing queue decreases. The results of this study elucidate the behavioral mechanism of purchasing mimicry and have further implications for understanding and improving dietary behaviors on campus.

Related papers

• Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocation [51.687404103375506]
  We partner with a non-profit organization in the state of Indiana that leads emphFood Drop, a program that is designed to redirect rejected truckloads of food away from landfills and into food banks. Our goal in this partnership is to completely automate Food Drop. In doing so, we need a matching algorithm for making real-time decisions that strikes a balance between ensuring fairness for the food banks that receive the food and optimizing efficiency for the truck drivers.
  arXiv  Detail & Related papers  (2024-06-10T15:22:41Z)
• Biased Bytes: On the Validity of Estimating Food Consumption from Digital Traces [17.890674216192277]
  We quantify the link between dietary behaviors as captured via social media (Twitter) v.s. a food-tracking application (MyFoodRepo) We find that the food type distributions in social media v.s. food tracking diverge; e.g., bread is 2.5 times more frequent among consumed and tracked foods than on Twitter, whereas cake is 12 times more frequent on Twitter. Across food types, food posted on Twitter is perceived as tastier, more caloric, less healthy, less likely to have been consumed at home, more complex, and larger-portioned, compared to consumed and tracked foods.
  arXiv  Detail & Related papers  (2022-08-30T17:13:16Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies [24.246450472404614]
  This work empirically examines the causal effects of user-generated online reviews on a granular level. We consider multiple aspects, e.g., the Food and Service of a restaurant.
  arXiv  Detail & Related papers  (2021-12-19T22:29:02Z)
• Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study [35.304562448945184]
  We use logs of 38 million food purchases made over an 8-year period on the Ecole Polytechnique Federale de Lausanne (EPFL) university campus. We estimate causal effects from the passively observed log data. We identify foods whose purchase frequency is impacted significantly by the eating partner's healthiness of food choice.
  arXiv  Detail & Related papers  (2021-02-17T13:47:28Z)
• Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing [7.786904858719634]
  The social context of eating (alone, with friends, with family, with a partner, etc.) is an important self-reported feature that influences aspects such as food type, psychological state while eating, and the amount of food. In this work, we examine the relation between the social context of eating and passive sensing data from wearables and smartphones.
  arXiv  Detail & Related papers  (2020-11-23T19:45:55Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)
• Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism [70.85894675131624]
  We learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. We propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. We show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.
  arXiv  Detail & Related papers  (2020-03-09T07:41:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.