S3190 #COVID19: Twitter as a Resource for IBD Patients During a Global Pandemic
Bibliographic record
Abstract
INTRODUCTION: In December 2019, a novel corona virus (COVID-19) emerged from Wuhan, China and within 3 months caused a global pandemic with high morbidity and mortality. Inflammatory bowel disease (IBD) patients may be at particular risk given immunocompromised state. Since Twitter is a source of information sharing, this study evaluated frequency, content, and origin of Twitter messaging focused on COVID-19 and IBD. METHODS: Social Feed Manager software (version 2.0.0: GWU Libraries, 2019) accessed Twitter’s application programming interface to obtain user information, origin and content of social media posts related to COVID-19 and IBD between March 11 and 18, 2020. SFM queried Twitter for #COVID19 #CoronaVirus or #CoronaOutbreak AND any comment containing “inflammatory bowel disease”, “IBD”, “Crohn’s” or “Ulcerative Colitis.” Messages that did not reference key terms and non-English messages were excluded. Individual user descriptions, tweets, and linked websites were analyzed for content. Analysis was performed using Chi-square, with significance set at P < 0.05. RESULTS: 1623 of 1648 (98.5%) were analyzed. 938 (57%) were personal user accounts, 444 (27%) physician, 109 (7%) non-physician health professionals, 51 (3%) support groups, 43 (3%) professional organizations, remainder were commercial or unclassified. More tweets were from personal versus medical associated accounts (P < 0.0001). Content was mostly informational (76%, n = 1226, P < 0.0001), 7% (n = 89) were treatment related, 24% (n = 296) related to the IBD-Covid registry and 3% (n = 34) were symptom related. The remainder were 8% (n = 128) concerns, 4% (n = 67) support, 3% (n = 51) inquiry and 9% (n = 151) other. 327 tweets attached links; primarily linked to established medical resources (67%, n = 204, P < 0.0001). Tweeters spanned 6 continents with most common from United States (n = 524), United Kingdom (n = 316), European Union (n = 120), Canada (n = 116). There was a significant difference (P = 0.0001) in the rate of messaging from US users compared to others. CONCLUSION: This study revealed that Twitter is used globally to share information on IBD and COVID-19. While most tweets came from personal accounts, the majority of content was informational and links more often came from established sources. Twitter provided opportunities for patient advocacy, support, and up to date information on disease prevention and treatment in a rapidly changing environment. Twitter can be an easily accessible healthcare education tool in a global pandemic to disseminate information.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".