To support people trying to lose weight and stay healthy, more and more
fitness apps have sprung up including the ability to track both calories intake
and expenditure. Users of such apps are part of a wider ``quantified self''
movement and many opt-in to publicly share their logged data. In this paper, we
use public food diaries of more than 4,000 long-term active MyFitnessPal users
to study the characteristics of a (un-)successful diet. Concretely, we train a
machine learning model to predict repeatedly being over or under self-set daily
calories goals and then look at which features contribute to the model's
prediction. Our findings include both expected results, such as the token
``mcdonalds'' or the category ``dessert'' being indicative for being over the
calories goal, but also less obvious ones such as the difference between pork
and poultry concerning dieting success, or the use of the ``quick added
calories'' functionality being indicative of over-shooting calorie-wise. This
study also hints at the feasibility of using such data for more in-depth data
mining, e.g., looking at the interaction between consumed foods such as mixing
protein- and carbohydrate-rich foods. To the best of our knowledge, this is the
first systematic study of public food diaries.Comment: Preprint of an article appearing at the Pacific Symposium on
Biocomputing (PSB) 2016 in the Social Media Mining for Public Health
Monitoring and Surveillance trac