Temporal Orientation Changes Due to COVID-19 Pandemic: A Machine Learning Text Analysis
Bibliographic record
Abstract
This study examines how temporal orientation evolves over time, changes at the onset of the COVID-19 pandemic, and in the period following the pandemic’s onset. Based on event system theory, we claim that the onset of the pandemic creates a strong event because it is highly novel, disruptive, and critical. Applying machine learning text analysis technique, this study extracted past, present, and future temporal orientation out of a total of 2,502,748 tweets, from 3979 individuals over 78 months. Discontinuous Growth Modeling framework is then used to test the baseline trajectory temporal orientation, how COVID-19 causes immediate changes that diverge from the baseline trajectory at the onset of the pandemic, and what is the long-lasting recovery effect after the occurrence of the pandemic. Results showed that (a) all the three aspects of temporal orientations evolve over time before the event, (b) there were immediate increases or decreases in each temporal orientation at the onset of the pandemic, and (c) longitudinal recovery effects last for a long run after the pandemic. Moreover, age explained individual differences of all these change processes. Theoretical contributions to temporal orientation literature and event system theory were discussed. Methodological advancements such as the use of big data and machine learning text analysis were also discussed.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".