Understanding Weight Loss via Online Discussions: Content Analysis of Reddit Posts Using Topic Modeling and Word Clustering Techniques
Open Access
- 8 June 2020
- journal article
- research article
- Published by JMIR Publications Inc. in Journal of Medical Internet Research
- Vol. 22 (6), e13745
- https://doi.org/10.2196/13745
Abstract
Maintaining a healthy weight can reduce the risk of developing many diseases, including type 2 diabetes, hypertension, and certain types of cancers. Online social media platforms are popular among people seeking social support regarding weight loss and sharing their weight loss experiences, which provides opportunities for learning about weight loss behaviors. This study aimed to investigate the extent to which the content posted by users in the r/loseit subreddit, an online community for discussing weight loss, and online interactions were associated with their weight loss in terms of the number of replies and votes that these users received. All posts that were published before January 2018 in r/loseit were collected. We focused on users who revealed their start weight, current weight, and goal weight and were active in this online community for at least 30 days. A topic modeling technique and a hierarchical clustering algorithm were used to obtain both global topics and local word semantic clusters. Finally, we used a regression model to learn the association between weight loss and topics, word semantic clusters, and online interactions. Our data comprised 477,904 posts that were published by 7660 users within a span of 7 years. We identified 25 topics, including food and drinks, calories, exercises, family members and friends, and communication. Our results showed that the start weight (β=.823; P<.001), active days (β=.017; P=.009), and median number of votes (β=.263; P=.02), mentions of exercises (β=.145; P<.001), and nutrition (β=.120; P<.001) were associated with higher weight loss. Users who lost more weight might be motivated by the negative emotions (β=−.098; P<.001) that they experienced before starting the journey of weight loss. In contrast, users who mentioned vacations (β=−.108; P=.005) and payments (β=−.112; P=.001) tended to experience relatively less weight loss. Mentions of family members (β=−.031; P=.03) and employment status (β=−.041; P=.03) were associated with less weight loss as well. Our study showed that both online interactions and offline activities were associated with weight loss, suggesting that future interventions based on existing online platforms should focus on both aspects. Our findings suggest that online personal health data can be used to learn about health-related behaviors effectively.This publication has 57 references indexed in Scilit:
- Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary ApproachPLOS ONE, 2013
- Website Usage and Weight Loss in a Free Commercial Online Weight Loss Program: Retrospective Cohort StudyJournal of Medical Internet Research, 2013
- Features Predicting Weight Loss in Overweight or Obese Participants in a Web-Based Intervention: Randomized TrialJournal of Medical Internet Research, 2012
- Effect of Diet and Exercise, Alone or Combined, on Weight and Body Composition in Overweight‐to‐Obese Postmenopausal WomenObesity, 2012
- Motivation, self-determination, and long-term weight controlInternational Journal of Behavioral Nutrition and Physical Activity, 2012
- Growth trajectories of exercise self-efficacy in older adults: Influence of measures and initial status.Health Psychology, 2011
- Associations of Internet Website Use With Weight Change in a Long-term Weight Loss Maintenance ProgramJournal of Medical Internet Research, 2010
- Primary Prevention of Stroke by Healthy LifestyleJournal of the American College of Cardiology, 2008
- Primary Prevention of Coronary Heart Disease in Women through Diet and LifestyleThe New England Journal of Medicine, 2000
- Human agency in social cognitive theory.American Psychologist, 1989