P024 CROHN’S DISEASE MESSAGING ON TWITTER: WHO’S TALKING?
Bibliographic record
Abstract
Twitter, a popular social networking service, enables users to interact with messages (tweets) limited to 140 characters. It is an increasingly powerful mechanism to convey information. There is limited data on the use of Twitter for disseminating medical information. This study evaluated the utilization of Twitter for Crohn’s disease messaging. Social Feed Manager (SFM; version 1.10.0; GW University, 2017), a software that can query social media platforms, queried Twitter’s application programming interface to collect user information, origin, and frequency of messaging about Crohn’s during a 10 day period. SFM queried Twitter using the terms #IBD, #Crohns, #crohnsdisease, #crones, #chrons, #chronsdisease, and #cronesdisease. The collection was limited by Twitter’s determination relevance, allowable timeframe collection, and user’s misspelling of disease condition. 3810 tweets were collected. The elimination of retweeted messages resulted in 2030 unique tweets. There were 133 different users, of which 24.4% were patients, 20.5% foundations, and 19.2% physicians (Chart 1). In users who enabled geotagging, there were 9 countries of origin with 45.1% United Kingdom, 37.7% United States and 9% Canada. 145 tweets were resent (133 retweeted 2–24, 6 retweeted 25–49, 4 retweeted 50–99, 2 retweeted >100 times) with the initial message originating from foundations (32.1%), physicians (19.7%), IBD patients (14.6%) (Graph 2). Twitter’s platform can be used for dissemination of unfiltered medical information messaging by diverse worldwide users. It is important that physicians are aware of information sources that are utilized by individuals with Crohn’s disease. Additional study to analyze messaging content will improve the understanding the role that Twitter may have in providing medical information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".