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.

Asthma affects an estimated 300 million people across the world. Despite being a highly treatable condition using preventative inhalers, mortality rates remain unacceptably high, with lack of adherence to medications cited as a major cause. While various drivers for non-adherence have been considered in isolation, interactions between demographic, behavioural and situational factors have never been modelled in concert mostly due to the limited ability of traditional methods to group such a large variety of features. This was addressed in this paper through a non-linear modelling approach, leveraging a novel dataset obtained via online surveying of asthma patients. Application of traditional variable importance methods to examine explanatory factors, however, is not possible. This is due to the presence of high multicollinearity in the data, a highly common occurrence in big data, or any datasets which include a large number of input features. This results in insights being obfuscated by extensive shared information and non-linear interactions occurring across variables. To mitigate this, we introduce the first Grouped Feature approach to Model Class Reliance (Group-MCR), that is able to quantify the importance of specific variable sets in underpinning explanations. Cross-validated models achieve 71% accuracy, with Group-MCR revealing the importance of perceptual factors. Out of all the perceptual factors denial proves to be most predictive of non-adherence to asthma medication, indicating that public health interventions should not only target the physical aspects of asthma, but additionally focus on patients’ beliefs and perceptions as valuable parts of their treatment.

The links between anxiety (and negative mental heath outcomes in general) and socio-economic conditions have been the subject of a large number of studies. However, the underlying mechanisms that affect this relationship have not been fully elucidated, nor have they been extended to consider potential mediators in the form of individual differences/psychological traits. Interrogating over 8-million customers' loyalty card transactions from a major health retailer, this paper investigated two ideas: the potential to use health product purchases to detect anxiety distributions across geo-spatial regions in England; and exploring the relationship between these product health purchases, deprivation levels and personality traits. Specifically, this analysis examined the co-variation between: anxiety purchases within district level geo-spatial regions in the UK; mean deprivation levels; and personality traits, across those districts. Contrary to previous findings, results demonstrated a negative correlation between anxiety related purchases and deprivation, and a positive correlation between anxiety related purchases and conscientiousness. This indicated the complex nature of the various forms of anxiety and its underlying causes and drivers - but also highlighted the challenges faced by different demographics in treating its symptoms.

Patients' perceptions about their condition have a strong impact on not only adherence to medication, but also on how they view themselves in the light of their condition. Research implies that Twitter is a particularly rich source of perceptions, as patients frequently use internet for information sharing and support. However, Twitter contains a lot of noise in the form of tweets that do not relate to perceptions, but are rather generated to advertise research and corporate news and this kind of information could `pollute' perception analysis. This study examined methods that could be used to extract perception tweets, on the example of tweets related to asthma. We first demonstrated differences between perception and non-perception tweets in terms of their linguistic features, and then focused on filtering perceptions using the classification process. Results demonstrated that there is a significant difference between perceptions and non-perceptions: perception tweets are shorter, have less capital letters, less punctuation signs and less hashtags. These features also performed well in predicting perceptions. However, the bag of words approach had better results in distinguishing between perception and non-perception tweets and the best results were obtained using word-based frequency vectorization and by training a neural network based classifier. Future research could explore the synergy of these approaches.