Evidence map›Paper›PMID 40311119›Full record

ArticleJMIR infodemiology2025

Natural Language Processing and Machine Learning Techniques for Analyzing Conversations About Nutritional Yeasts in the United States and France: Retrospective Social Media Listening Study.

Jean-François Jeanne, Joelle Malaab, Antoine Vanhove, Florian Mourey, Manissa Talmatkadi, Stéphane Schück

Abstract read
In one paragraph

Article in JMIR infodemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jean-François JeanneGnosis by Lesaffre, Lesaffre Group, Marcq-en-Baroeul, France.ORCID 0000-0002-5074-4679
Joelle MalaabKap Code, 4 rue de Cléry, Paris, 75002, France, 33 01 44 82 74 74.ORCID 0000-0002-4489-0071
Antoine VanhoveGnosis by Lesaffre, Lesaffre Group, Marcq-en-Baroeul, France.ORCID 0009-0006-3487-2052
Florian MoureyGnosis by Lesaffre, Lesaffre Group, Marcq-en-Baroeul, France.ORCID 0000-0002-0826-9632
Manissa TalmatkadiKap Code, 4 rue de Cléry, Paris, 75002, France, 33 01 44 82 74 74.ORCID 0000-0002-8019-6159
Stéphane SchückKap Code, 4 rue de Cléry, Paris, 75002, France, 33 01 44 82 74 74.ORCID 0000-0003-2642-7726

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nutritional yeast, an inactive form of Saccharomyces cerevisiae, has recently become increasingly popular as a food supplement and healthy ingredient, especially among individuals following plant-based diets. It is valued for its health benefits and high content of B vitamins, minerals, and protein. Social media has enabled people to share information and personal experiences at an unprecedented level, further amplifying conversations around health and nutrition. With the rise of social media, data mining techniques like natural language processing and machine learning are increasingly used for analyzing the large amounts of information generated on these platforms. Objective: This study aimed to analyze social media data from the United States and France to identify the most frequently discussed topics among nutritional yeast consumers. The objective was to fill gaps in our understanding of the perceptions, experiences, and usage trends related to nutritional yeast. Methods: This study was retrospective, using social media data geolocated in the United States and France, posted by users discussing nutritional yeast between December 2017 and September 2023. Data cleaning and filtering were done using natural language processing methods and specific algorithms. Biterm topic modeling was applied to identify the most frequently discussed topics. Results: A total of 36,642 posts written by 28,069 users discussing nutritional yeast were identified across 1039 publicly available online sources. This included 34,292 posts from the United States (26,154 users across 994 sources) and 2350 from France (n=1915 users across 45 sources). Twitter was the most commonly used platform in both countries, accounting for 39.6% of posts in the United States (13,587/34,292) and 84.3% in France (1982/2350). In the United States, conversations centered around the role of nutritional yeast role as a vegan nutrient source (n=12,345, 36.0%). Several users highlighted its culinary versatility as a natural seasoning (n=8093, 23.6%) and its health and skin benefits (n=6173, 18.0%). In France, discussions frequently focused on nutritional yeast's use in dietary supplement routines in various forms (n=1177, 50.1%), emphasizing its benefits alongside other supplements such as castor oil, particularly noted for effects on nails and hair (n=928, 39.5%). Conclusions: This social media listening study identified the perceptions and preferences of nutritional yeast users in France and the United States. Researchers and health care professionals can reflect on these findings to investigate the potential health benefits of nutritional yeast for specific groups and its long-term impact on different diets and lifestyles. Marketers may also use this information to create customized strategies that better align with the preferences and needs of each market.

Indexed as

Dietary SupplementsMachine LearningNatural Language ProcessingSaccharomyces cerevisiaeSocial MediaFranceHumansRetrospective StudiesUnited Statesinfodemiology studymachine learningnatural language processingnutritional yeastsocial media listening

Identifiers

PMID40311119
PMCPMC12061346

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.