A Cloud Based Framework for Identification of Influential Health Experts from Twitter
Bibliographic record
Abstract
The ever increasing growth in health related data has necessitated the development of pervasive tools and technologies to manage the huge data volumes. Likewise, the conventional healthcare services are transforming into patient-centric services to offer ubiquitous access to the health related information. However, there is a need to extend the capabilities of the existing health services and tools so that users could become aware about their health, devise wellness plans, and seek experts' advice at no or low cost using the social media. In this paper, we propose a cloud based framework that uses Twitter data to offer recommendations about the most influential health experts. We employ a variant of the Hyperlink-Induced Topic Search (HITS) approach to identify the candidate health experts based on health related keywords used in the tweets. Subsequently, we propose an influence metric that calculates the influence of the candidate experts based on various parameters. The proposed approach attained high accuracy when compared to other approaches for expert user identification. Moreover, experimental results exhibit that the approach is highly scalable for workloads of varying sizes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".