#AskRenal: Use of an Automated Twitter Account to Crowdsource Nephrology Queries
Bibliographic record
Abstract
Background: Social media platforms are used in contemporary crowdsourcing, and Twitter is apt for reaching a large number of people with a common interest. Users, especially those with a small follower count may find it challenging to reach a large audience. #AskRenal was developed as a Twitter crowdsourcing tool to help users get answers to nephrology questions. We hypothesized that the #AskRenal hashtag could be used by anyone to receive helpful and timely responses to simple or complex nephrology questions posed on the social media platform. Methods: A Twitter account @AskRenal, and an online Twitter bot that automatically retweeted any new tweets containing the hashtag #AskRenal were created. Using the Symplur Healthcare Hashtag tool, we extracted and analyzed public Twitter content containing the hashtag #AskRenal posted between Dec 2016 to Aug 2020. Tweets were excluded if they were duplicates, retweets, or if the tweet content was not the form of an original question. A group of 15 medical professionals reviewed #AskRenal tweets individually and a 10-question survey was completed for each one. Results: During the study period, there were 17,704 tweets containing the hashtag #AskRenal and 3099 were included in the survey analysis. We found that 40% (1228/3099) of #AskRenal questions were posed by users with < 1000 followers and 9% (270/3099) were from students and trainees. The questions were spread across a wide range of nephrology topics. Over 75% (2386/3099) of the #AskRenal questions garnered a response, and answers came quickly with 69% (1644/2386) receiving a reply within 6 hours of posting. The reviewers found these responses to be helpful in answering the original questions 83% (1978/2386) of the time. The inclusion of hyperlinks and images in the reply was associated with a helpful answer (p < 0.001) and a higher follower count was not significantly associated with the probability of obtaining a helpful answer. Conclusions: We demonstrated that a targeted hashtag and a dedicated Twitter account that retweets the hashtag automatically can be used to garner timely and helpful responses by a wide range of individuals, irrespective of follower count, seeking answers to nephrology questions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".