The Impact of Social Media on Otolaryngology Literature: Analyzing the Correlation Between Altmetrics and Citation Count
Bibliographic record
Abstract
Abstract Objective The altmetric attention score (AAS) is an alternative metric that tracks article sharing via online platforms, reflecting an article's online attention trend. The objective of this study was to analyze the impact of social media on Otolaryngology–Head and Neck Surgery (OHNS) literature and analyze the correlation between AAS and citation count. Study Design and Setting A retrospective review of otolaryngology journal article citation data and Altmetric attention score. Methods The top 10 OHNS journals with highest impact factors were identified using the Journal Citation Reports (JCR). The number of citations in 2018 and 2019 were extracted from JCR and AAS was extracted from the altmetrics website. The primary outcome of this study was to establish whether a correlation between AAS and citation count exists, and whether AAS could serve as a valid alternative metric to assess the quality of individual articles. Results By analyzing data from 3729 articles, a weak statistically significant positive correlation was identified between AAS and citation count (r = 0.18, P < .001), and between number of citations and Twitter activity (r = 0.18, P < .001). In addition, a statistically significant strong correlation was seen between Twitter activity and AAS (r = 0.79, P < .001). Conclusion The current results clearly illustrate a weak correlation between AAS and citations and between Twitter activity and citations. Due to various limitations, the use of AAS should be limited to serve as a complementary metric to the current gold standard rather than an alternative metric.
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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 | 0.018 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".