JMIR Publications

ABSTRACT

Background:

Emotional eating is one of the significant symptoms of various eating disorders. It has been difficult to collect their behavioral data offline in large quantities, so studies of the symptoms themselves have only been done in part. To provide adequate support for users with emotional eating symptoms, we must understand their behavior patterns to design sophisticated supporting system.

Objective:

In this study, we aim to analyze behavior patterns of emotional eaters (EE) as a first step to design appropriate intervention/supporting system. We also explored the feedback patterns depending on the EE behavior topic to diagnose the problem of online feedbacks.

Methods:

Machine learning framework and LDA topic modeling tool were used to collect and analyze behavioral data of emotional eating. We collected data from /r/loseit, which is a sub-community of Reddit community using Python Reddit API wrapper. We constructed a dataset that includes all posts and the feedback between July 2014 and May 2018.This dataset consists of 185,950 posts and 3,528,107 comments. Deleted and improperly collected data were eliminated. We developed the SGD based machine learning classifier to collect refined behavioral data of users with emotional eating behaviors. The expert group including medical doctors who specializes in EE diagnosis and a nutritionist who has profound knowledge in EE behavior labeled sets of data that are needed to train machine learning classifiers. The experts labeled 5,126posts to EE (coded as 1) and others (coded as 0). Finally, the topic modeling process was conducted with Latent Dirichlet allocation (LDA).

Results:

As a result, we found four macro-perspective topics of online EE behaviors which are 1)addressing feeling, 2)sharing physical changes, 3)sharing/asking for dietary information, and 4)sharing dietary strategies. We also found linguistic evidence regarding each topic. We also discovered feedback topics and patterns depending on the behavior topic of emotional eating. Five main topics of the feedback are 1)dietary information, 2)compliment, 3)sympathy, 4)health information, and 5)the automatic bot feedback. Feedback topic distribution significantly differs depending on the types of emotional eating behavior.

Conclusions:

Our work introduces a data-driven approach for analyzing behavior patterns of users with EE behaviors. We discovered the possibilities of LDA topic model as an exploratory user study method for abnormal user behaviors in medical research. We also investigated the possibilities of machine learning and topic modeling based classifiers to automatically categorize linguistic behavioral data which could be applied to personalized medicine in further research.