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The Federal Menu Labeling Law and Twitter Discussions about Calories in the United States: An Interrupted Time-Series Analysis

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  • Yulin Hswen

    (Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA
    Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
    Computational Epidemiology Lab, Harvard Medical School, Boston, MA 02215, USA
    Innovation Program, Boston Children’s Hospital, Boston, MA 02215, USA)

  • Alyssa J. Moran

    (Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MA 21205, USA)

  • Siona Prasad

    (Harvard University, Cambridge, MA 02138, USA)

  • Anna Li

    (Harvard University, Cambridge, MA 02138, USA)

  • Denise Simon

    (Harvard Pilgrim Health Care, Boston, MA 02215, USA)

  • Lauren Cleveland

    (Harvard Pilgrim Health Care, Boston, MA 02215, USA)

  • Jared B. Hawkins

    (Computational Epidemiology Lab, Harvard Medical School, Boston, MA 02215, USA
    Innovation Program, Boston Children’s Hospital, Boston, MA 02215, USA)

  • John S. Brownstein

    (Computational Epidemiology Lab, Harvard Medical School, Boston, MA 02215, USA
    Innovation Program, Boston Children’s Hospital, Boston, MA 02215, USA)

  • Jason Block

    (Harvard Pilgrim Health Care, Boston, MA 02215, USA)

Abstract

Public awareness of calories in food sold in retail establishments is a primary objective of the menu labeling law. This study explores the extent to which we can use social media and internet search queries to understand whether the federal calorie labeling law increased awareness of calories. To evaluate the association of the federal menu labeling law with tweeting about calories we retrieved tweets that contained the term “calorie(s)” from the CompEpi Geo Twitter Database from 1 January through 31 December in 2016 and 2018. Within the same time period, we also retrieved time-series data for search queries related to calories via Google Trends (GT). Interrupted time-series analysis was used to test whether the federal menu labeling law was associated with a change in mentions of “calorie(s)” on Twitter and relative search queries to calories on GT. Before the implementation of the federal calorie labeling law on 7 May 2018, there was a significant decrease in the baseline trend of 4.37 × 10 −8 (SE = 1.25 × 10 −8 , p < 0.001) mean daily ratio of calorie(s) tweets. A significant increase in post-implementation slope of 3.19 × 10 −8 (SE = 1.34 × 10 −8 , p < 0.018) mean daily ratio of calorie(s) tweets was seen compared to the pre-implementation slope. An interrupted time-series (ITS) analysis showed a small, statistically significant upward trend of 0.0043 (SE = 0.036, p < 0.001) weekly search queries for calories pre-implementation, with no significant level change post-implementation. There was a decrease in trend of 1.22 (SE = 0.27, p < 0.001) in search queries for calories post-implementation. The federal calorie labeling law was associated with a 173% relative increase in the trend of mean daily ratio of tweets and a -28381% relative change in trend for search queries for calories. Twitter results demonstrate an increase in awareness of calories because of the addition of menu labels. Google Trends results imply that fewer people are searching for the calorie content of their meal, which may no longer be needed since calorie information is provided at point of purchase. Given our findings, discussions online about calories may provide a signal of an increased awareness in the implementation of calorie labels.

Suggested Citation

  • Yulin Hswen & Alyssa J. Moran & Siona Prasad & Anna Li & Denise Simon & Lauren Cleveland & Jared B. Hawkins & John S. Brownstein & Jason Block, 2021. "The Federal Menu Labeling Law and Twitter Discussions about Calories in the United States: An Interrupted Time-Series Analysis," IJERPH, MDPI, vol. 18(20), pages 1-11, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10794-:d:656329
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    References listed on IDEAS

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    4. Eui-Ki Kim & Jong Hyeon Seok & Jang Seok Oh & Hyong Woo Lee & Kyung Hyun Kim, 2013. "Use of Hangeul Twitter to Track and Predict Human Influenza Infection," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
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