Online Impact and Presence of a Specialized Social Media Team for the Journal of Neurosurgery: Descriptive Analysis
Bibliographic record
Abstract
BACKGROUND: Social media use continues to gain momentum in academic neurosurgery. To increase journal impact and broaden engagement, many scholarly publications have turned to social media to disseminate research. The Journal of Neurosurgery Publishing Group (JNSPG) established a dedicated, specialized social media team (SMT) in November 2016 to provide targeted improvement in digital outreach. OBJECTIVE: The goal of this study was to examine the impact of the JNSPG SMT as measured by increased engagement. METHODS: We analyzed various metrics, including impressions, engagements, retweets, likes, profile clicks, and URL clicks, from consecutive social media posts from the JNSPG's Twitter and Facebook platforms between February 1, 2015 and February 28, 2019. Standard descriptive statistics were utilized. RESULTS: Between February 2015 and October 2016, when a specialized SMT was created, 170 tweets (8.1 tweets/month) were posted compared to 3220 tweets (115.0 tweets/month) between November 2016 and February 2019. All metrics significantly increased, including the impressions per tweet (mean 1646.3, SD 934.9 vs mean 4605.6, SD 65,546.5; P=.01), engagements per tweet (mean 35.2, SD 40.6 vs mean 198.2, SD 1037.2; P<.001), retweets (mean 2.5, SD 2.8 vs mean 10.5, SD 15.3; P<.001), likes (mean 2.5, SD 4.0 vs mean 18.0, SD 37.9; P<.001), profile clicks (mean 1.5, SD 2.0 vs mean 5.2, SD 43.3; P<.001), and URL clicks (mean 13.1, SD 14.9 vs mean 38.3, SD 67.9; P<.001). Tweets that were posted on the weekend compared to weekdays had significantly more retweets (mean 9.2, SD 9.8 vs mean 13.4, SD 25.6; P<.001), likes (mean 15.3, SD 17.9 vs mean 23.7, SD 70.4; P=.001), and URL clicks (mean 33.4, SD 40.5 vs mean 49.5, SD 117.3; P<.001). Between November 2015 and October 2016, 49 Facebook posts (2.3 posts/month) were sent compared to 2282 posts (81.5 posts/month) sent between November 2016 and February 2019. All Facebook metrics significantly increased, including impressions (mean 5475.9, SD 5483.0 vs mean 8506.1, SD 13,113.9; P<.001), engagements (mean 119.3, SD 194.8 vs mean 283.8, SD 733.8; P<.001), and reach (mean 2266.6, SD 2388.3 vs mean 5344.1, SD 8399.2; P<.001). Weekend Facebook posts had significantly more impressions per post (mean 7967.9, SD 9901.0 vs mean 9737.8, SD 19,013.4; P=.03) and a higher total reach (mean 4975.8, SD 6309.8 vs mean 6108.2, SD 12,219.7; P=.03) than weekday posts. CONCLUSIONS: Social media has been established as a crucial tool for the propagation of neurosurgical research and education. Implementation of the JNSPG specialized SMT had a demonstrable impact on increasing the online visibility of social media content.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".