Understanding Weight Loss via Online Discussions: Content Analysis of Reddit Posts Using Topic Modeling and Word Clustering Techniques (Preprint)

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 <i>r/loseit</i> 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; <i>P</i>&lt;.001), active days (β=.017; <i>P</i>=.009), and median number of votes (β=.263; <i>P</i>=.02), mentions of exercises (β=.145; <i>P</i>&lt;.001), and nutrition (β=.120; <i>P</i>&lt;.001) were associated with higher weight loss. Users who lost more weight might be motivated by the negative emotions (β=−.098; <i>P</i>&lt;.001) that they experienced before starting the journey of weight loss. In contrast, users who mentioned vacations (β=−.108; <i>P</i>=.005) and payments (β=−.112; <i>P</i>=.001) tended to experience relatively less weight loss. Mentions of family members (β=−.031; <i>P</i>=.03) and employment status (β=−.041; <i>P</i>=.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.