Recent work has demonstrated the value of social media monitoring for health surveillance (e.g., tracking influenza or depression rates). It is an open question whether such data can be used to make causal inferences (e.g., determining which activities lead to increased depression rates). Even in traditional, restricted domains, estimating causal effects from observational data is highly susceptible to confounding bias. In this work, we estimate the effect of exercise on mental health from Twitter, relying on statistical matching methods to reduce confounding bias. We train a text classifier to estimate the volume of a user’s tweets expressing anxiety, depression, or anger, then compare two groups: those who exercise regularly (identified by their use of physical activity trackers like Nike+), and a matched control group. We find that those who exercise regularly have significantly fewer tweets expressing depression or anxiety; there is no significant difference in rates of tweets expressing anger. We additionally perform a sensitivity analysis to investigate how the many experimental design choices in such a study impact the final conclusions, including the quality of the classifier and the construction of the control group.


  author =       {Virgile Landeiro Dos Reis and Aron Culotta},
  title =        {Using Matched Samples to Estimate the Effects of Exercise\\ on Mental Health from Twitter},
  booktitle = {Twenty-ninth National Conference on Artificial Intelligence (AAAI)},
  year =         2015