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
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.
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“many social data analyses rely on machine learned classifiers to classify first, and count later (e.g., Mislove et al., 2011; Zagheni et al., 2014; Abbar et al., 2015).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Abbar et al., 2015
Citations
Cites
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Fischler, C. (1988) · Social Science Information · doi:10.1177/053901888027002005 · Cited by 1,343
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Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003) · Journal of Economic Perspectives · doi:10.1257/089533003769204371 · Cited by 1,312
Romero, D. M., Meeder, B., & Kleinberg, J. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963503 · Cited by 1,082
Social network thresholds in the diffusion of innovations
Valente, T. W. (1996) · Social Networks · doi:10.1016/0378-8733(95)00256-1 · Cited by 1,041
You Are What You Tweet: Analyzing Twitter for Public Health
Paul, M., & Dredze, M. H. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14137 · Cited by 947
Takhteyev, Y., Gruzd, A., & Wellman, B. (2012) · Social Networks · doi:10.1016/j.socnet.2011.05.006 · Cited by 615
Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
Aramaki, E., Maskawa, S., & Morita, M. (2011) · Cited by 515
Flavor network and the principles of food pairing
Ahn, Y.-Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A.-L. (2011) · Scientific Reports · doi:10.1038/srep00196 · Cited by 487
Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic
Cohen-Cole, E., & Fletcher, J. M. (2008) · Journal of Health Economics · doi:10.1016/j.jhealeco.2008.04.005 · Cited by 479
Research works printed but not linked: 8
- Aramaki E., 2011, EMNLP
- Cawley J., 2012, The medical care costs of obesity: an instrumental variables approach. Journal of health economics 31, 1
- Donohew L., 1990, Public health campaigns: Individual message strategies and a model. Communication and health: Systems and applications
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Cited by
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
The Geography and Importance of Localness in Geotagged Social Media
Johnson, I. L., Sengupta, S., Schöning, J., & Hecht, B. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858122 · Cited by 67
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
- Cited by
- 165
- References
- 28
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- Abbar, S., Mejova, Y., & Weber, I. (2015). You Tweet What You Eat: Studying Food Consumption Through Twitter. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2702123.2702153
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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.