Abstract
Twitter data has been shown broadly applicable for public health surveillance. Previous public heath 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.
This work was conducted during the first author’s research intern at NYU Center for Data Science.
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App Spring Inc. List Challenges: Food, https://www.listchallenges.com/lists/food.
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Zhang, X., Athanasiadou, R., Razavian, N. (2021). Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study. In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2020 2020. Lecture Notes in Computer Science(), vol 12633. Springer, Cham. https://doi.org/10.1007/978-3-030-71055-2_12
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