Space, time, and disease on social media: a case study of dengue fever in China
Bibliographic record
Abstract
It is possible to generate real-time and location-by-location data of many types of human dynamic events based on social media information for the awareness of events in public health. Analyzing these events is useful in understanding spatiotemporal trends and patterns of how diseases spread and also provides indications for users’ sentiment about the concerned disease. This article examines the spatial and temporal patterns of social media posts based on the content, attributes, and follower activities of posts on social media. We describe the spatial features of the topic discussed in the posts and the spatial relationship among comments on the posts. We present models for describing the diffusion process of these posts and for exploring their spatiotemporal patterns. Our results suggest that (1) the long-term trends of the topics in users’ views seem to be stable, (2) results from analyzing follower activities of posts are critical in describing the spatial patterns of the posts, and (3) the diffusion process of an event in social media is still similar to that of a traditional information diffusion model. Our findings are useful for understanding social media and social events. The processes we describe in this article suggest a standard form of analysis that can be adopted for extracting spatiotemporal patterns of information diffusion and for data mining in social media posts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".