A comprehensive analysis of #Enuresis conversation on Twitter
Bibliographic record
Abstract
INTRODUCTION: We sought to perform a quantitative and qualitative analysis of online Twitter discussion of enuresis using the hashtag #Enuresis. METHODS: Symplur, a fee-based Twitter analytics service, was employed to aggregate and analyze Twitter activity, users, and content for #Enuresis, the official Twitter hashtag for enuresis, between June 2016 and November 2018. Twitter activity was analyzed using average tweets and new users per month. Users were classified based on geographic location, occupation, and organizational affiliation. Content analysis was performed by retrieving information about Twitter engagement metrics, including retweets, links, media, mentions, replies, and frequently used words and hashtags. RESULTS: A total of 3133 tweets and 1555 users utilizing #Enuresis were identified between June 28, 2016 and November 28, 2018. The average ± standard deviation (SD) number of tweets using #Enuresis per month were not significantly different from 2016 through 2018 (p=0.292). The number of users increased from six to 1555 during the study period, but there was no statistically significant increase in number of new users per month (p=0.346). Physicians comprised 14% of the top 100 influencers, followed by medical device organizations (13%). Popular hashtags in #Enuresis tweets were #Bedwetting, #PisEnLaCama, #schoolnurses, #helpingkids, #ninos, and #salud. Hyperlinks used in #Enuresis tweets included advocacy, academic, commercial, and other social media websites. CONCLUSIONS: Our analysis of #Enuresis demonstrates that the online Twitter discussion regarding enuresis is growing. These results indicate that enuresis has a global appeal and has especially gained traction in European countries, as well as in the U.S.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".