Literature DB >> 33946659

Analysis of Social Media Discussions on (#)Diet by Blue, Red, and Swing States in the U.S.

Amir Karami1, Alicia A Dahl2, George Shaw2, Sruthi Puthan Valappil3, Gabrielle Turner-McGrievy4, Hadi Kharrazi5, Parisa Bozorgi4,6.   

Abstract

The relationship between political affiliations and diet-related discussions on social media has not been studied on a population level. This study used a cost- and -time effective framework to leverage, aggregate, and analyze data from social media. This paper enhances our understanding of diet-related discussions with respect to political orientations in U.S. states. This mixed methods study used computational methods to collect tweets containing "diet" or "#diet" shared in a year, identified tweets posted by U.S. Twitter users, disclosed topics of tweets, and compared democratic, republican, and swing states based on the weight of topics. A qualitative method was employed to code topics. We found 32 unique topics extracted from more than 800,000 tweets, including a wide range of themes, such as diet types and chronic conditions. Based on the comparative analysis of the topic weights, our results revealed a significant difference between democratic, republican, and swing states. The largest difference was detected between swing and democratic states, and the smallest difference was identified between swing and republican states. Our study provides initial insight on the association of potential political leanings with health (e.g., dietary behaviors). Our results show diet discussions differ depending on the political orientation of the state in which Twitter users reside. Understanding the correlation of dietary preferences based on political orientation can help develop targeted and effective health promotion, communication, and policymaking strategies.

Entities:  

Keywords:  diet; health; politics; social media; text mining

Year:  2021        PMID: 33946659     DOI: 10.3390/healthcare9050518

Source DB:  PubMed          Journal:  Healthcare (Basel)        ISSN: 2227-9032


  30 in total

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Authors:  Martin Loef; Harald Walach
Journal:  Prev Med       Date:  2012-06-24       Impact factor: 4.018

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3.  Understanding the different types of social support offered by audience to A-list diary-like and informative bloggers.

Authors:  Hsiu-Chia Ko; Li-Ling Wang; Yi-Ting Xu
Journal:  Cyberpsychol Behav Soc Netw       Date:  2013-01-30

4.  Body-Weight Fluctuations and Outcomes in Coronary Disease.

Authors:  Sripal Bangalore; Rana Fayyad; Rachel Laskey; David A DeMicco; Franz H Messerli; David D Waters
Journal:  N Engl J Med       Date:  2017-04-06       Impact factor: 91.245

5.  Health Benefits and Adverse Effects of a Gluten-Free Diet in Non-Celiac Disease Patients.

Authors:  Benjamin Niland; Brooks D Cash
Journal:  Gastroenterol Hepatol (N Y)       Date:  2018-02

6.  Communication about childhood obesity on Twitter.

Authors:  Jenine K Harris; Sarah Moreland-Russell; Rachel G Tabak; Lindsay R Ruhr; Ryan C Maier
Journal:  Am J Public Health       Date:  2014-05-15       Impact factor: 9.308

7.  Disseminating research information through Facebook and Twitter (DRIFT): presenting an evidence-based framework.

Authors:  Gemma Ryan; Hanah Sfar-Gandoura
Journal:  Nurse Res       Date:  2018-05-02

8.  Social determinants of health equity.

Authors:  Michael Marmot; Jessica J Allen
Journal:  Am J Public Health       Date:  2014-09       Impact factor: 9.308

Review 9.  User's guide to correlation coefficients.

Authors:  Haldun Akoglu
Journal:  Turk J Emerg Med       Date:  2018-08-07

10.  Interrater reliability: the kappa statistic.

Authors:  Mary L McHugh
Journal:  Biochem Med (Zagreb)       Date:  2012       Impact factor: 2.313

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  1 in total

1.  Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach.

Authors:  Heng Xu; Jun Zeng; Zhaodan Tai; Huihui Hao
Journal:  Healthcare (Basel)       Date:  2022-01-20
  1 in total

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