A Framework for Online Public Health Debates: Some Design Elements for Visual Analytics Systems
Bibliographic record
Abstract
Nowadays, many people are deeply concerned about their physical well-being; as a result, they invest much time and effort investigating health-related topics. In response to this, many online websites and social media profiles have been created, resulting in a plethora of information on such topics. In a given topic, oftentimes, much of the information is conflicting, resulting in online camps that have different positions and arguments. We refer to the collection of all such positionings and entrenched camps on a topic as an online public health debate. The information people encounter regarding such debates can ultimately influence how they make decisions, what they believe, and how they act. Therefore, there is a need for public health stakeholders (i.e., people with a vested interest in public health issues) to be able to make sense of online debates quickly and accurately. In this paper, we present a framework-based approach for investigating online public health debates—a preliminary work that can be expanded upon. We first introduce the concept of online debate entities (ODEs), which is a generalization for those who participate in online debates (e.g., websites and Twitter profiles). We then present the framework ODIN (Online Debate entIty aNalyzer), in which we identify, define, and justify ODE attributes that we consider important for making sense of online debates. Next, we provide an overview of four online public health debates (vaccines, statins, cannabis, and dieting plans) using ODIN. Finally, we showcase four prototype visual analytics systems whose design elements are informed by the ODIN framework.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".