Threshold of Depression Measure in the Framework of Sentiment Analysis of Tweets: Managing Risk during a Crisis Period Like the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has had a devastating impact on the world. The surge in the number of daily new cases and deaths around the world and in South Africa, in particular, has increased fear, psychological breakdown, and uncertainty among the population during the COVID-19 pandemic period, leading many to resort to prayer, meditation, and the consumption of religious media as coping measures. This study analyzes social media data to examine the perceptions and attitudes of the South African community toward religion as well as their well-being appreciation during the COVID-19 period. We extract four sets of tweets related to COVID-19, religion, life purpose, and life experience, respectively, by users within the geographical area of South Africa and compute their sentiment scores. Then, a Granger causality test is conducted to assess the causal relationship between the four time series. While the findings reveal that religious sentiment scores Granger-causes life experience, COVID-19 similarly Granger-causes life experience, illustrating some shifts experienced within the community during the crisis. This study further introduces for the first time a Threshold of Depression measure in the sentiment analysis framework to assist in managing the risk induced by extremely negative sentiment scores. Risk management during a period of crisis can be a hectic task, especially the level of distress or depression the community is experiencing in order to offer adequate mental support. This can be assessed through the Conditional Threshold of Depression which quantifies the threshold of depression of a community conditional on a given variable being at its Threshold of Depression. The findings indicate that the well-being indicators (life purpose and life experience) provide the highest values of this threshold and could be used to monitor the emotions of the population during periods of crisis to support the community in crisis management.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".