Ill-fated interactions: modeling complaints on a food waste fighting platform

The redistribution of surplus food is a challenging problem, yet a crucial one to address given the urgent nature of climate change. However, designing computer-mediated food sharing systems is made even harder due to failed interactions between users and ensuing complaints, which can dissuade others from participating when shared within a public forum. To examine the phenomenon of complaints within such data, we analyze the public forum of a food sharing platform, OLIO. We characterize complaining behaviour and augment it through qualitative labeling and a machine learning approach to model complaints using affective indicators of dissatisfaction across a corpus of 3,195 forum posts. Results emphasize that linguistic features yield high prediction accuracies, with negative, nonconstructive sentiment being of greatest relevance. We discuss how machine learning can further enrich qualitative understandings and validation of complaints in the sharing economy.