research-article

Characterizing Dietary Choices, Nutrition, and Language in Food Deserts via Social Media

Authors:

Munmun De Choudhury,

Emre KicimanAuthors Info & Claims

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing

Pages 1157 - 1170

https://doi.org/10.1145/2818048.2819956

Published: 27 February 2016 Publication History

Abstract

Social media has emerged as a promising source of data for public health. This paper examines how these platforms can provide empirical quantitative evidence for understanding dietary choices and nutritional challenges in “food deserts” -- Census tracts characterized by poor access to healthy and affordable food. We present a study of 3 million food related posts shared on Instagram, and observe that content from food deserts indicate consumption of food high in fat, cholesterol and sugar; a rate higher by 5-17% compared to non-food desert areas. Further, a topic model analysis reveals the ingestion language of food deserts to bear distinct attributes. Finally, we investigate to what extent Instagram ingestion language is able to infer whether a tract is a food desert. We find that a predictive model that uses ingestion topics, socio-economic and food deprivation status attributes yields high accuracy (>80%) and improves over baseline methods by 6-14%. We discuss the role of social media in helping address inequalities in food access and health.

References

[1]

Sofiane Abbar, Yelena Mejova, and Ingmar Weber. 2015. You Tweet What You Eat: Studying Food Consumption Through Twitter. In Proc. CHI.

Digital Library

[2]

Philippe Apparicio, Marie-Soleil Cloutier, and Richard Shearmur. 2007. The case of Montreal's missing food deserts: evaluation of accessibility to food supermarkets. Intl. journal of health geographics 6, 1 (2007), 4.

[3]

Christopher M Bishop and others. 2006. Pattern recognition and machine learning. Vol. 4. Springer.

Digital Library

[4]

Troy C Blanchard and Todd L Matthews. 2007. Retail Concentration, Food Deserts, and Food-Disadvantaged Communities in Rural America. Remaking the North American food system: Strategies for sustainability (2007), 201.

[5]

Peter Clifford, Sylvia Richardson, and Denis He Ìmon. 1989. Assessing the significance of the correlation between two spatial processes. Biometrics (1989), 123–134.

[6]

Aron Culotta. 2014. Estimating county health statistics with Twitter. In Proc. CHI. ACM, 1335–1344.

Digital Library

[7]

Steven Cummins. 2002. Food deserts. The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society (2002).

[8]

Steven Cummins and Sally Macintyre. 1999. The location of food stores in urban areas: a case study in Glasgow. British Food Journal 101, 7 (1999), 545–553.

[9]

Munmun De Choudhury, Scott Counts, and Eric Horvitz. 2013. Major life changes and behavioral markers in social media: case of childbirth. In Proc. CSCW. ACM, 1431–1442.

Digital Library

[10]

Munmun De Choudhury, Scott Counts, Eric Horvitz, and Aaron Hoff. 2014. Characterizing and Predicting Postpartum Depression from Facebook Data. In Proc. CSCW. ACM.

Digital Library

[11]

Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting depression via social media. In Proc. ICWSM.

[12]

Virgile Landeiro Dos Reis and Aron Culotta. 2015. Using Matched Samples to Estimate the Effects of Exercise on Mental Health from Twitter. In Proc. AAAI.

Digital Library

[13]

Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, and Dane Bell. 2014. Analyzing the language of food on social media. In Proc. IEEE Big Data. 778–783.

[14]

Deja Hendrickson, Chery Smith, and Nicole Eikenberry. 2006. Fruit and vegetable access in four low-income food deserts communities in Minnesota. Agriculture and Human Values 23, 3 (2006), 371–383.

[15]

Matthew Hoffman, Francis R Bach, and David M Blei. 2010. Online learning for latent dirichlet allocation. In Proc. NIPS. 856–864.

[16]

Christopher M Homan, Naiji Lu, Xin Tu, Megan C Lytle, and Vincent Silenzio. 2014. Social structure and depression in TrevorSpace. In Proc. CSCW. 615–625.

Digital Library

[17]

Dan Immergluck. 2009. Large redevelopment initiatives, housing values and gentrification: the case of the Atlanta Beltline. Urban Studies 46, 8 (2009), 1723–1745.

[18]

Sue Jamison-Powell, Conor Linehan, Laura Daley, Andrew Garbett, and Shaun Lawson. 2012. I can't get no sleep: discussing# insomnia on twitter. In Proc. CHI. 1501–1510.

Digital Library

[19]

David M Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. The parable of Google Flu: traps in big data analysis. Science (2014).

[20]

Sally Macintyre. 2007. Deprivation amplification revisited; or, is it always true that poorer places have poorer access to resources for healthy diets and physical activity? International Journal of Behavioral Nutrition and Physical Activity 4, 1 (2007), 32.

[21]

Yelena Mejova, Hamed Haddadi, Anastasios Noulas, and Ingmar Weber. 2015. # FoodPorn: Obesity Patterns in Culinary Interactions. In Proc. Digital Health.

Digital Library

[22]

Latetia V Moore and Ana V Diez Roux. 2006. Associations of neighborhood characteristics with the location and type of food stores. American journal of public health 96, 2 (2006), 325–331.

[23]

Megan A Moreno, Dimitri A Christakis, Katie G Egan, Libby N Brockman, and Tara Becker. 2011. Associations between displayed alcohol references on Facebook and problem drinking among college students.

[24]

ives of pediatrics & adolescent medicine (2011), Archpediatrics–2011.

[25]

Elizabeth L Murnane and Scott Counts. 2014. Unraveling abstinence and relapse: smoking cessation reflected in social media. In Proc. CHI. ACM, 1345–1354.

Digital Library

[26]

Kit Oldham, Peter Blecha, and HistoryLink (Firm). 2011. The Story of the Port of Seattle: Rising Tides and Tailwinds: 1911-2011. University of Washington Press.

[27]

Minsu Park, David W McDonald, and Meeyoung Cha. 2013. Perception Differences between the Depressed and Non-depressed Users in Twitter. In Proc. ICWSM.

[28]

Michael J Paul and Mark Dredze. 2011. You Are What You Tweet: Analyzing Twitter for Public Health. In Proc. ICWSM.

[29]

Tim Pearson, Jean Russell, Michael J Campbell, and Margo E Barker. 2005. Do food deserts influence fruit and vegetable consumption?A cross-sectional study. Appetite 45, 2 (2005), 195–197.

[30]

Dan Pelleg, Andrew W Moore, and others. 2000. X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In ICML. 727–734.

Digital Library

[31]

Mark Petticrew, Steven Cummins, Catherine Ferrell, Anne Findlay, Cassie Higgins, Caroline Hoy, Adrian Kearns, and Leigh Sparks. 2005. Natural experiments: an underused tool for public health? Public health 119, 9 (2005), 751–757.

[32]

Daniele Quercia, Jonathan Ellis, Licia Capra, and Jon Crowcroft. 2012. Tracking gross community happiness from tweets. In Proc. CSCW. ACM, 965–968.

Digital Library

[33]

Paul R Rosenbaum and Donald B Rubin. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 39, 1 (1985), 33–38.

[34]

Adam Sadilek, Henry A Kautz, and Vincent Silenzio. 2012. Modeling Spread of Disease from Social Interactions. In Proc. ICWSM.

[35]

Kai A Schafft, Eric B Jensen, and C Clare Hinrichs. 2009. Food deserts and overweight schoolchildren: Evidence from Pennsylvania. Rural Sociology 74, 2 (2009), 153–177.

[36]

Hansen Andrew Schwartz, Johannes C Eichstaedt, Margaret L Kern, and Lukasz et al. Dziurzynski. 2013. Characterizing Geographic Variation in Well-Being Using Tweets. In Proc. ICWSM.

[37]

Sanket Sharma and Munmun De Choudhury. 2015. Detecting and Characterizing Nutritional Information of Food and Ingestion Content in Instagram. In Proc. WWW Companion.

Digital Library

[38]

George Davey Smith and Eric Brunner. 1997. Socio-economic differentials in health: the role of nutrition. Proc. nutrition society 56, 1A (1997), 75–90.

[39]

Edison Thomaz, Cheng Zhang, Irfan Essa, and Gregory D Abowd. 2015. Inferring Meal Eating

[40]

vities in Real World Settings from Ambient Sounds: A Feasibility Study. In Proceedings of the 20th International Conference on Intelligent User Interfaces. ACM, 427–431.

[41]

Sho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, and Hiroyuki Ohsaki. 2015. Recognizing Depression from Twitter Activity. In Proc. CHI. 3187–3196.

Digital Library

[42]

Alessandro Venerandi, Giovanni Quattrone, Licia Capra, Daniele Quercia, and Diego Saez-Trumper. 2015. Measuring Urban Deprivation from User Generated Content. In Proc. CSCW.

Digital Library

[43]

Claudia Wagner, Philipp Singer, and Markus Strohmaier. 2014. Spatial and temporal patterns of online food preferences. In Proc. WWW Companion. 553–554.

Digital Library

[44]

Renee E Walker, Christopher R Keane, and Jessica G Burke. 2010. Disparities and access to healthy food in the United States: a review of food deserts literature. Health & place 16, 5 (2010), 876–884.

[45]

Robert West, Ryen W White, and Eric Horvitz. 2013. From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In Proc. WWW. 1399–1410.

Digital Library

[46]

Amanda Whelan, Neil Wrigley, Daniel Warm, and Elizabeth Cannings. 2002. Life in a'food desert'. Urban Studies 39, 11 (2002), 2083–2100.

[47]

Martin White, Jane Bunting, Liz Williams, Simon Raybould, Ashley Adamson, and John Mathers. 2004. Do food deserts exist? A multi-level, geographical analysis of the relationship between retail food access, socio-economic position and dietary intake. Food Standards Authority, London (2004).

[48]

Christopher Winship and Stephen L Morgan. 1999. The estimation of causal effects from observational data. Annual review of sociology (1999), 659–706.

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cover image ACM Conferences

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing

February 2016

1866 pages

ISBN:9781450335928

DOI:10.1145/2818048

General Chairs:
Darren Gergle
Northwestern University
,
Meredith Ringel Morris
Microsoft Research
,
Program Chairs:
Pernille Bjørn
University of Copenhagen
,
Joseph Konstan
University of Minnesota

Copyright © 2016 ACM.

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Published: 27 February 2016

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https://doi.org/10.58208/cphs.1435790
Gligorić KZbinden RChiolero AKıcıman EWhite RHorvitz EWest R(2024)Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to actionFrontiers in Nutrition10.3389/fnut.2024.123107011Online publication date: 4-Jun-2024
https://doi.org/10.3389/fnut.2024.1231070
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