Sharing Health Information on Facebook: Practices, Preferences, and Risk Perceptions of North American Users
Bibliographic record
Abstract
Motivated by the benefits, people have used a variety of webbased services to share health information (HI) online. Among these services, Facebook, which enjoys the largest population of active subscribers, has become a common place for sharing various types of HI. At the same time, Facebook was shown to be vulnerable to various attacks, resulting in unintended information disclosure, privacy invasion, and information misuse. As such, Facebook users face the dilemma of benefiting from HI sharing and risking their privacy. In this work, we investigate HI sharing practices, preferences, and risk perceptions among Facebook users. We interviewed 21 participants with chronic health conditions to identify the key factors that influence users' motivation to share HI on Facebook. We then conducted an online survey with 492 Facebook users in order to validate, refine, and extend our findings. While some factors related to sharing HI were found in literature, we provide a deeper understanding of the main factors that influenced users' motivation to share HI on Facebook. The results suggest that the gained benefits from prior HI sharing experiences, and users' overall attitudes toward privacy, correlate with their motivation to disclose HI. Furthermore, we identify other factors, specifically users' perceived health and the audience of the shared HI, that appear to be linked with users' motivation to share HI. Finally, we suggest design improvements— such as anonymous identity as well as search and recommendation features—for facilitating HI sharing on Facebook and similar sites.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".