#Trending: Intermittent Fasting Is a Global Discussion on Twitter
Bibliographic record
Abstract
Introduction: Individuals are increasingly using intermittent fasting to optimize weight. The concept of intermittent fasting has transcended into social media platforms which are often used to disseminate medical information. It is important that clinicians are aware of the dietary recommendations discussed on social media. This study evaluated frequency, origin and content of Twitter messaging focused on intermittent fasting. Methods: Social Feed Manager software (SFM; version 1.10.0: GW University, 2017) accessed Twitter's application programming interface to obtain user information, origin and frequency of messaging related to intermittent fasting for 7 days. SFM queried Twitter using the terms intermittent fasting, #intermittentfasting, intermitent fasting, #intermitent fasting, alternate day fasting, #alternatedayfasting, time restricted feeding or #eTRF. Geotag feature identified origin of tweets. Duplicate entries, retweets and non-English messages were excluded. Word frequency analysis was performed using Voyant Tools' Cirrus application (Sinclair, Rockwell, Voyant Tools Team, 2012). Statistical analysis was performed using Chi-square, with significance set at p<0.05. Results: 7962 tweets were obtained with 5275 analyzed. 5163 (97.9%) were from personal, 57 (1.1%) from physician and 55 (1%) from non-physician health professional accounts. There was a significant difference (p=0.0001) in the rate of tweeting about intermittent fasting between personal and health professional user. 3368 messages could be analyzed for region of origin, revealing that primary messaging was from US (1952; 58%), United Kingdom (690; 20.5%) and Canada (204; 6.1%). There was a significant difference (p=0.0001) in the rate of messaging from US users compared to others. Word analysis revealed that the primary terms did not include medical advice, physician or health care professional. Conclusion: This study confirmed that Twitter is used globally to share information on intermittent fasting as a method to optimize weight. While this study was limited by Twitter's determination of relevance, allowable time for collection and user's misspelling, it is important to recognize that social media platforms offer easily accessible, unfiltered information. While health care professionals were not significant contributors to the intermittent fasting Twitter discussion, it is important that health care professionals are aware of the use of Twitter to disseminate health information.1036_A Figure 1. Worldwide Twitter Messaging about Intermittent Fasting.1036_B Figure 2. Cirrus Word Frequency Analysis of Intermittent Fasting Messages
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".