  Twitter data has been shown broadly applicable for public health
surveillance. Previous public health studies based on Twitter data have largely
relied on keyword-matching or topic models for clustering relevant tweets.
However, both methods suffer from the short-length of texts and unpredictable
noise that naturally occurs in user-generated contexts. In response, we
introduce a deep learning approach that uses hashtags as a form of supervision
and learns tweet embeddings for extracting informative textual features. In
this case study, we address the specific task of estimating state-level obesity
from dietary-related textual features. Our approach yields an estimation that
strongly correlates the textual features to government data and outperforms the
keyword-matching baseline. The results also demonstrate the potential of
discovering risk factors using the textual features. This method is
general-purpose and can be applied to a wide range of Twitter-based public
health studies.
