Senti-COVID19: An Interactive Visual Analytics System for Detecting Public Sentiment and Insights Regarding COVID-19 From Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As governments take measures against COVID-19, the epidemic situation is expected to improve, but public sentiment is likely to fluctuate during this process, potentially influencing the best course of action. Social media has become a prevalent way for the public to express emotions and opinions in recent times. So that, the sentiment analysis on top of it may detect and provide valuable evidence of public attitude and help governments subsequent formulation of measures and policies. We present Senti-COVID19, an interactive visual analytic system for reflecting and analyzing public sentiment and detecting sentiment fluctuation triggers on social media. Senti-COVID19 adopts lexicon-based sentiment analysis to divulge the public opinion to COVID-19 events, employing libraries to extract keywords and statistics for providing detailed information. In addition, it offers visualizations for presenting the analysis, allowing users to quickly discover relevant information. Our results show that Senti-COVID19 can be used effectively to analyze sentiment from social media text, allowing users to explore derived data and identify insights from the collected tweets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it