Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data
Why this work is in the frame
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Bibliographic record
Abstract
The novel COVID-19 is one of the most serious health pandemics in our time. According to the World Health Organization (WHO), it has been spread over more than 150 countries and territories worldwide with thousands of deaths. In this research, we propose a framework to explore the dynamics and flow of behavioral changes among twitter users during the pandemic. In our framework, the related tweets are retrieved from the Twitter social network in three different time intervals and stored in our data repository. After cleaning and pre-processing the data, using natural language processing and social network analysis techniques, a set of emotions is extracted from them along with their sentiment characteristics. Further, the data is visualized in order to identify the changing patterns. The results of this project show significant connections between the infection and mortality rates and the emotional characteristics of the twitter users.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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