Insights from antiaging-related X discussions: A six-year #Longevity hashtag analysis study
Bibliographic record
Abstract
• 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".