A mobile application for conscientious consumption based on collective intelligence

Abstract

Food directly affects the population health and well-being. Although food packages provide information on their ingredients and additives, consumers do not know for sure what they are acquiring. Lots of information are published in social networks; many studies are contradictory; many reports and posts are questionable; many jargons are used; the official classifications of some components are inconsistent; and doctors and nutritionists recommend attention to consumption of various substances. Allied to this, individuals may have specific restrictions because of dietary needs, diseases, allergies, philosophical beliefs, environmental concerns, etc. In this context, our goal is to develop a mobile collaborative system to collectively generate an aggregated database, easily accessible, that consumers may have at the time they need to make a conscious decision about of what to consume. Various research lines are needed to develop this kind of application. In particular, it is necessary an in-depth understanding on what are the information needs that consumers have about the food products they consume, on how a mobile collaborative system can provide such information, on how to promote collaborative filtering of information and how to properly recognize products and their ingredients. In this project, we aim to conduct scientific and technological research to design and prototype a collaborative mobile application to provide this information. For this, we will conduct qualitative research with users, collecting data through interviews and surveys and analyzing the data using grounded theory procedures. We will also perform a domain analysis, which combined with the empirical study and literature of the area, will support the requirements analysis and design conception, which will follow the recommendations of participatory design and design science. System prototypes will be developed to evaluate models, technologies and design decisions. In addition, we will investigate the application of data mining techniques, image recognition, and natural language processing in this context. As result of this project, we expect a greater understanding on consumers' information needs and their decision-making process related to a healthy food, a domain analysis, a design proposal, a proposal of requirements for this kind of application, a functional prototype, and a definition of what techniques and technologies are appropriate in this context. As an effect of using this type of system, we expect a greater awareness of healthy eating and lifestyle, a reduction of disease and causalities, a change of attitude of food manufacturers and faster response of regulatory agencies. We also hope to collect a database that can be explored in future research. (AU)