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Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter

Author

Listed:
  • Theo Lynn

    (Irish Institute of Digital Business, Dublin City University, Dublin, Ireland)

  • Pierangelo Rosati

    (Irish Institute of Digital Business, Dublin City University, Dublin, Ireland)

  • Guto Leoni Santos

    (Centro de Informática, Universidade Federal de Pernambuco, Recife 52071-030, Brazil)

  • Patricia Takako Endo

    (Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife 50100-010, Brazil)

Abstract

Over 2.8 million people die each year from being overweight or obese, a largely preventable disease. Social media has fundamentally changed the way we communicate, collaborate, consume, and create content. The ease with which content can be shared has resulted in a rapid increase in the number of individuals or organisations that seek to influence opinion and the volume of content that they generate. The nutrition and diet domain is not immune to this phenomenon. Unfortunately, from a public health perspective, many of these ‘influencers’ may be poorly qualified in order to provide nutritional or dietary guidance, and advice given may be without accepted scientific evidence and contrary to public health policy. In this preliminary study, we analyse the ‘healthy diet’ discourse on Twitter. While using a multi-component analytical approach, we analyse more than 1.2 million English language tweets over a 16-month period in order to identify and characterise the influential actors and discover topics of interest in the discourse. Our analysis suggests that the discourse is dominated by non-health professionals. There is widespread use of bots that pollute the discourse and seek to create a false equivalence on the efficacy of a particular nutritional strategy or diet. Topic modelling suggests a significant focus on diet, nutrition, exercise, weight, disease, and quality of life. Public health policy makers and professional nutritionists need to consider what interventions can be taken in order to counteract the influence of non-professional and bad actors on social media.

Suggested Citation

  • Theo Lynn & Pierangelo Rosati & Guto Leoni Santos & Patricia Takako Endo, 2020. "Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-28, November.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8557-:d:447100
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    References listed on IDEAS

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    1. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    2. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    3. Dokyun Lee & Kartik Hosanagar & Harikesh S. Nair, 2018. "Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook," Management Science, INFORMS, vol. 64(11), pages 5105-5131, November.
    4. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    5. repec:aph:ajpbhl:10.2105/ajph.2016.303512_4 is not listed on IDEAS
    6. Kristina Eriksson-Backa & Kim Holmberg & Stefan Ek, 2016. "Communicating diabetes and diets on Twitter - a semantic content analysis," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 16(1), pages 8-24.
    7. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    8. Reema Aswani & Arpan Kumar Kar & P. Vigneswara Ilavarasan, 2018. "Detection of Spammers in Twitter marketing: A Hybrid Approach Using Social Media Analytics and Bio Inspired Computing," Information Systems Frontiers, Springer, vol. 20(3), pages 515-530, June.
    9. Martin Quinn & Theodore Lynn & Stephen Jollands & Binesh Nair, 2016. "Domestic Water Charges in Ireland - Issues and Challenges Conveyed through Social Media," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3577-3591, August.
    10. Sinnenberg, L. & Buttenheim, A.M. & Padrez, K. & Mancheno, C. & Ungar, L. & Merchant, R.M., 2017. "Twitter as a tool for health research: A systematic review," American Journal of Public Health, American Public Health Association, vol. 107(1), pages 1-8.
    11. Karami, Amir & Dahl, Alicia A. & Turner-McGrievy, Gabrielle & Kharrazi, Hadi & Shaw, George, 2018. "Characterizing diabetes, diet, exercise, and obesity comments on Twitter," International Journal of Information Management, Elsevier, vol. 38(1), pages 1-6.
    12. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    13. Campbell, Donald T., 1979. "Assessing the impact of planned social change," Evaluation and Program Planning, Elsevier, vol. 2(1), pages 67-90, January.
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    2. Suzanne Elayan & Martin Sykora, 2024. "Digital intermediaries in pandemic times: social media and the role of bots in communicating emotions and stress about Coronavirus," Journal of Computational Social Science, Springer, vol. 7(3), pages 2481-2504, December.

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