JMIR Publications

ABSTRACT

Background:

Emotional eating is one of the most significant symptoms of various eating disorders. It has been difficult to collect large behavioral data offline, so only partial studies of this symptom has been conducted. To provide adequate support for the online social media users with emotional eating (EE) symptoms, we must understand their behavior patterns to design a sophisticated personalized support system (PSS).

Objective:

In this study, we aim to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system.

Methods:

Machine learning framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data of emotional eating (EE). Data from a sub-community of Reddit,/r/loseit, was analyzed. This dataset included all posts and feedbacks between July 2014 and May 2018, consisting of 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic Gradient Descent (SGD) based machine learning classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the machine learning classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge in EE behavior. The experts labeled 5,126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with Latent Dirichlet allocation (LDA).

Results:

The following four macro-perspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: 1) addressing feelings, 2) sharing physical changes, 3) sharing/asking for dietary information, and 4) sharing dietary strategies. Five main topics of feedbacks were 1) dietary information, 2) compliments, 3) consolation, 4) automatic bot feedback, and 5) health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P value < .001).

Conclusions:

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