Quantified Self Meets Social Media: Sharing of Weight Updates on Twitter

TL;DR

This study analyzes how sharing weight data on Twitter can predict individual health metrics and social behaviors, revealing patterns useful for public health monitoring and personalized interventions.

Contribution

It uniquely combines quantified self data with social media activity to predict health status and behavior patterns using publicly available data.

Findings

Social media features can predict individual weight.

Quantified self behaviors show weekly and monthly patterns.

Online social behaviors correlate with physical health metrics.

Abstract

An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these Quantified Selfs many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social surroundings and general online behavior. Concretely, we look at which social media features are predictive of physical status, such as body weight at the individual level, and activity patterns at the population level. We show that it is possible to predict an individual's weight using their online social behaviors, such as their self-description and tweets. Weekly and monthly patterns of quantified-self behaviors are also discovered. These findings could contribute to building models to monitor public health and to have more customized personal training interventions. While there are many studies using either quantified self or social media data in isolation, this is one of the few that combines the two data sources and, to the best of our knowledge, the only one that uses public data.