To mitigate sparsity, we also included topics generated using Latent Dirichlet Allocation (LDA) (Blei et al., 2003) and all tweets collected by Fried et al. For example, one of the generated topics contains words that approximate the standard American diet (e.g., chicken, potatoes, cheese, baked, beans, fried, mac), which has already been shown to correlate with higher overweight and T2DM rates<cite> (Fried et al., 2014</cite> Figure 2 : A decision tree from the random forest classifier trained using state-level Twitter data.