Identifying food insecurity in food sharing networks via machine learning

Nica-Avram, Georgiana; Harvey, John; Smith, Gavin; Smith, Andrew; Goulding, James

Abstract

© 2020 Elsevier Inc. Food insecurity in the UK has captured public attention. However, estimates of its prevalence are deeply contentious. The lack of precision on the volume of emergency food assistance currently provided to those in need is made even more ambiguous due to increasing use of peer-to-peer food sharing systems (e.g. OLIO). While these initiatives exist as a solution to food waste rather than food poverty, they are nonetheless carrying a hidden share of the food insecurity burden, with the socio-economic status of technology-assisted food sharing donors, volunteers, and recipients remaining obscure. In this article we examine the relationship between food sharing and deprivation generally, before applying machine learning techniques to develop a predictive model of food insecurity based upon aggregated food sharing behaviours by OLIO users in the UK. We demonstrate that data from food sharing systems can help quantify a previously hidden aspect of deprivation and we make the case for a reformed approach to modelling food insecurity.

• Journal Article Type: Article
• Acceptance Date: Sep 13, 2020
• Online Publication Date: Oct 15, 2020
• Publication Date: 2021-07
• Deposit Date: Nov 10, 2020
• Publicly Available Date: Apr 16, 2022
• Journal: Journal of Business Research
• Print ISSN: 0148-2963
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 131
• Pages: 469-484
• DOI: https://doi.org/10.1016/j.jbusres.2020.09.028
• Public URL: https://nottingham-repository.worktribe.com/output/5032516
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0148296320306123?via%3Dihub