• Principal investigators: Kaiyang Qin, Saar Mollen, Wilma Waterlander, and Eline Smit
  • Summary: Social media represents an emerging dimension of the digital food environment. The digital food environment on social media is mainly built upon two sources: food-relevant advertisements from companies (ads) and food-relevant posts from consumers. Together, we call this food-relevant content. This food-relevant content may vary in healthfulness. In our research, we examine whether people are exposed to more unhealthy food-relevant content than healthy food-relevant content on social media platforms. And to what extent this is due to companies pushing (unhealthy) food content to social media users or because of users’ own interaction with food-related content that may lead to self-selection of more of such content. In sum, the present study aims to map the digital food environment on social media by exploring users’ interactions with two sources of (unhealthy) food content: food ads and food posts. To this end, we collected participants’ YouTube data, and we tested their social media interactions with exposure to the food content, as well as with the number of food companies that have targeted participants as their customers. In the present study, we investigated the following questions:
    1. What does the digital food environment look like?
    2. How do digital food content interactions (e.g., liking, searching) affect digital food content exposure?
    3. How do food companies affect digital food content exposure?
  • Data collection period: April – November 2023
  • Participant recruitment: Participants were recruited from Panelinzicht (an online panel company) and a lab recruitment system from UvA.
  • Instructions: Instructions to perform the data access and download can be found here.
  • Codebook: This study included a questionnaire that was carried out by Qualtrics. The codebook of the questionnaire is available upon request at the PI.
  • Ethical review: This study was approved by the Ethics Review Board, University of Amsterdam (Reference: FMG-472). The informed consent can be found here, and the privacy policy can be found here.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the Netflix data is available on Github.
  • Data: Please contact the PI of this study.
  • Acknowledgements: This project was funded by the Healthy Future initiative of the University of Amsterdam.