The Current State of Online Social Networking for the Health Community
Bibliographic record
Abstract
In this paper, we discuss the prevalence of misleading information in health-oriented online social networks and discussion boards. With increasing numbers of patients and caregivers browsing online for insights into how to address their speci c health problems, and with a growing tendency to value the opinions of peers when making choices about healthcare solutions, it is important for computer science researchers to develop strategies that can be introduced to enable each person to be better informed. We begin with a brief report on some of the activity currently observed in online communities. From here, we advocate the use of trust modeling, an approach examined by arti cial intelligence researchers in the sub eld of multi-agent systems. In particular, we sketch some speci c solutions to integrate, based on frameworks that we have developed which have been validated as e ective in presenting bene cial messages to users. We conclude with a view to the future, both with respect to re nement of our trust modeling solutions, and with respect to engagement of government, healthcare providers and individuals.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".