Towards Bottom-Up Analysis of Social Food

in ACM Digital Health Conference 201

Towards Bottom-Up Analysis of Social Food

in ACM Digital Health Conference 201

Scraping social media photos posted in Kenya and elsewhere to detect and analyze food types

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ∼30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019.Accepted manuscrip

Social Media Analytics in Food Innovation and Production: a Review

Until recently social media and social media analytics (SMA) were basically used only for communication and marketing purposes. However, thanks to advances in digital technologies and big data analytics, potential applications of SMA extend now to production processes and overall business management. As a result, SMA has become an important tool for gaining and sustaining competitive advantage across various sectors, industries and end-markets. Yet, the food industry still lags behind when it comes to the use of digital technologies and advanced data analytics. A part of the explanation lies in the limited knowledge of potential applications of SMA in food innovation and production. The aim of this paper is to provide a review of literature on possible uses of SMA in the food industry sector and to discuss both the benefits, risks, and limitations of SMA in food innovation and production. Based on the literature review, it is concluded that mining social media data for insights can create significant business value for the food industry enterprises and food service sector organizations. On the other hand, many proposals for using SMA in the food domain still await direct experimental tests. More research and insights concerning risks and limitations of SMA in the food sector would be also needed. The issue of responsible data analytics as part of Corporate Digital Responsibility and Corporate Social Responsibility of enterprises using social media data for food innovation and production also requires a greater attention

Modelling on Social Media: Influencing Young Adults’ Food Choices

The aim of this study was to investigate whether social media influence young adults’ food choices through social modelling. Before the main study, a pilot was conducted to improve and develop scales for measuring the influence of social modelling, as well as some control variables. In the main study, 354 young adult participants (ages 18-35) were recruited through social media and completed an online questionnaire. They were randomly assigned to a modelling or control condition by choosing one of two colors and were then either exposed to a series of Instagram screenshots depicting modelling of eating behaviour (modelling condition) or Instagram screenshots depicting the same meal on its own (control condition). The participants were then asked about their attitudes, perception of healthiness and likelihood of consumption of the meal presented. The degree to which participants use social media to make food-related decisions and their interest in the healthiness of their food were used as control variables. Results showed that there was no significant difference between attitudes, consumption, or health perception in the two conditions. Limitations of the study and their possible influence on the results are discussed, as are suggestions for future research