SEARCHCROSS-SECTIONALnutrition, public health

cross-sectional·nutrition, public health, epidemiology, research methods·PMC10603558

Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis

Journal of Medical Internet Research · 5 authors, 2 centres

AI SUMMARY

FIDELITY 100%

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

“

TL;DR

This study analyzed 504 tweets about precision nutrition from late 2020 to characterize the content, author expertise, and prevalence of misinformation on Twitter. It found that tweets frequently contained hype and untrue information, especially in nutrigenomics topics, while authors with scientific or medical expertise posted more credible content.

Full summary

177 CHARS

About a quarter of authors (26.2%) presented as science or medicine experts. Tweets from authors with science/medicine expertise had higher engagement (more retweets and likes).

STUDY TYPE

cross-sectional

SPECIALTY

nutrition

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10603558

Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis

Journal of Medical Internet Research

READ THE FULL PAPER

ABSTRACT

Abstract Background It is possible that tailoring dietary approaches to an individual’s genomic profile could provide optimal dietary inputs for biological functioning and support adherence to dietary management protocols. The science required for such nutrigenetic and nutrigenomic profiling is not yet considered ready for broad application by the scientific and medical communities; however, many personalized nutrition products are available in the marketplace, creating the potential for hype and misleading information on social media. Twitter provides a unique big data source that provides real-time information. Therefore, it has the potential to disseminate evidence-based health information, as well as misinformation. Objective We sought to characterize the landscape of precision nutrition content on Twitter, with a specific focus on nutrigenetics and nutrigenomics. We focused on tweet authors, types of content, and presence of misinformation. Methods Twitter Archiver was used to capture tweets from September 1, 2020, to December 1, 2020, using keywords related to nutrition and genetics. A random sample of tweets was coded using quantitative content analysis by 4 trained coders. Codebook-driven, quantified information about tweet authors, content details, information quality, and engagement metrics were compiled and analyzed. Results The most common categories of tweets were precision nutrition products and nutrigenomic concepts. About a quarter (132/504, 26.2%) of tweet authors presented themselves as science experts, medicine experts, or both. Nutrigenetics concepts most frequently came from authors with science and medicine expertise, and tweets about the influence of genes on weight were more likely to come from authors with neither type of expertise. A total of 14.9% (75/504) of the tweets were noted to contain untrue information; these were most likely to occur in the nutrigenomics concepts topic category. Conclusions By evaluating social media discourse on precision nutrition on Twitter, we made several observations about the content available in the information environment through which individuals can learn about related concepts and products. Tweet content was consistent with the indicators of medical hype, and the inclusion of potentially misleading and untrue information was common. We identified a contingent of users with scientific and medical expertise who were active in discussing nutrigenomics concepts and products and who may be encouraged to share credible expert advice on precision nutrition and tackle false information as this technology develops. Keywords: nutrigenetics, nutrigenomics, precision nutrition, Twitter, credibility, misinformation, content analysis

CITATION EXPORT

Sapna Batheja, Emma M Schopp, Samantha Pappas et al.. Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis. Journal of Medical Internet Research. (2023). https://doi.org/10.2196/43701

AUTHORS

5 · 2 CENTRES

Sapna Batheja, Emma M Schopp, Samantha Pappas, Siri Ravuri, Susan Persky

1Department of Nutrition and Food Studies, George Mason University, Fairfax, VA, United States
2Social and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, MD, United States