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Record W4407986881 · doi:10.1016/j.crbiot.2025.100280

Insights from antiaging-related X discussions: A six-year #Longevity hashtag analysis study

2025· article· en· W4407986881 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCurrent Research in Biotechnology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsLongevityGerontologyPsychologyMedicine

Abstract

fetched live from OpenAlex

• Study of tweets containing the hashtag #Longevity on X over six years (August 2018 – August 2024). • A total of 382,032 tweets posted from 109,935 users were analyzed. • The sentiment was predominantly positive but showed a slight decline during and after the COVID-19 pandemic. • Health, aging, wellness, and fitness were dominant themes in #Longevity-tagged tweets. • Nicotinamide mononucleotide, rapamycin, and green tea were the most frequently mentioned supplements and drugs. As social media platforms continue to play an increasingly significant role in shaping public discourse and disseminating scientific information, understanding how longevity and aging-related topics are discussed online has become crucial for researchers and healthcare professionals. This study investigates the global discourse on longevity and aging through the analysis of the hashtag #Longevity on the social media platform X (formerly Twitter) over a six-year period from August 1, 2018, to August 1, 2024. A total of 382 032 posts were shared by 109 935 users across 200 countries. The analysis focused on revealing key themes, geographical distribution, sentiment analysis, and the most frequently mentioned supplements and drugs related to longevity. The results show a high level of engagement with the hashtag, primarily driven by users from the United States, followed by the United Kingdom and Canada. Sentiment analysis revealed predominantly positive attitudes towards longevity-related topics, with a slight but statistically significant (p < 0.0001) decline during and after the COVID-19 pandemic. The study identified nicotinamide mononucleotide, rapamycin, and green tea as the most frequently mentioned supplements or drugs in longevity discussions. Notably, there was a significant increase in discussions about niacin derivatives, particularly nicotinamide mononucleotide, during and after the pandemic period. This study highlights the importance of social media as a tool for gauging public interest and sentiment towards scientific topics like longevity, providing valuable insights for researchers, healthcare professionals, and policymakers to enhance science communication and public engagement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.014
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.465
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it