Discussions of Antibiotic Resistance on Social Media Platforms: Text Mining and Mixed Methods Content Analysis Study
Bibliographic record
Abstract
Background: With the increasing popularity of web 2.0 apps, social media has made it possible for individuals to post messages on antibiotic ineffectiveness. In such online conversations, patients discuss their quality of life (QoL). Social media have become key tools for finding and disseminating medical information. Objective: To identify the main themes of discussion, the difficulties encountered by patients with respect to antibiotic ineffectiveness and the impact on their QoL (physical, psychological, social, or financial). Methods: A noninterventional retrospective study was carried out by collecting social media posts in French language written by internet users mentioning their experience with antibiotics, and the impact of their ineffectiveness on their QoL. Messages posted between January 2014 and July 2020 were extracted from French-speaking publicly available online forums. Results: A total of 3773 messages were included in the analysis corpus after extraction and filtering. These messages were posted by 2335 individual web users, most of them being women around 35 years of age. Inefficacy of treatment options and the lack of information regarding the use of antibiotics were among the most discussed topics. QoL was discussed in 63% of the 3773 messages posted. The most common is the physical impact (78%). Patients discussed the persistence of symptoms and adverse effects. The second kind of impact is psychological (65%), characterized by feelings of anxiety or despair about the situation. Conclusions: This social media analysis allowed us to identify a strong impact of the perceived ineffectiveness of antibiotic therapy on patients' daily life particularly in terms of physical and psychological consequences. These results provide health care experts information directly generated by patients regarding their own experiences. Social media studies constitute a complementary source of evidence that could be used to optimize messages to the public about appropriate use of antibiotics.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".