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Cited by 165

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

You Tweet What You Eat: Studying Food Consumption Through Twitter

Abbar, Sofiane, Mejova, Yelena, Weber, Ingmar·2015·Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
DOI 10.1145/2702123.2702153
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Abstract

Food is an integral part of our lives, cultures, and well-being, and is of major interest to public health. The collection of daily nutritional data involves keeping detailed diaries or periodic surveys and is limited in scope and reach. Alternatively, social media is infamous for allowing its users to update the world on the minutiae of their daily lives, including their eating habits. In this work we examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the state-wide obesity rates, achieving a Pearson correlation of 0.77 across the 50 US states and the District of Columbia. We then build a model to predict county-wide obesity and diabetes statistics based on a combination of demographic variables and food names mentioned on Twitter. Our results show significant improvement over previous CHI research (Culotta 2014). We further link this data to societal and economic factors, such as education and income, illustrating that areas with higher education levels tweet about food that is significantly less caloric. Finally, we address the somewhat controversial issue of the social nature of obesity (Christakis & Fowler 2007) by inducing two social networks using mentions and reciprocal following relationships.

Overview

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Registry

Crossref · 10.1145/2702123.2702153

Cites

28 of 28 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 8

  1. Aramaki E., 2011, EMNLP
  2. Cawley J., 2012, The medical care costs of obesity: an instrumental variables approach. Journal of health economics 31, 1
  3. Donohew L., 1990, Public health campaigns: Individual message strategies and a model. Communication and health: Systems and applications
  4. Fischler C., 1988, self and identity. Social science information 27, 2
  5. Paul M. J., 2011, ICWSM
  6. Schwartz H. A., 2013, ICWSM
  7. Silva T. H., 2014, ICWSM
  8. West R., 2013, WWW

Cited by

See all 165

Metadata

Authors
Abbar, S., Mejova, Y., & Weber, I.
Affiliations
Qatar Computing Research Institute, Doha, Qatar
Venue
Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2015-04-18
Pages
3197-3206
DOI
10.1145/2702123.2702153
Cited by
165
References
28
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.