Social Media and Crisis Management During the Covid 19 Pandemic: An Analysis of the Twitter Activity of Five Key Ghanaian State Actors in the First Year of the Pandemic
Bibliographic record
Abstract
The covid 19 pandemic led to a public health crisis which was responsible for the death of more than four hundred and fifty thousand people. Governments worldwide devised many strategies to help slow down the spread of the virus and reduce its impact on the economy and livelihoods of people. Even though social media platforms played a key role in information dissemination and awareness creation in relation to the novel Corona Virus, it is unknown if the activity of key government social media accounts have any relationship with the number of recorded cases. The researchers used a quantitative content analysis strategy to analyse the posts of 5 key Ghanaian government accounts on Twitter between 11th March 2020 and 11th March 2021, in relation to certain Covid 19 keywords. The researchers found that, no correlation exists between the Twitter posts of key government accounts and number of recorded Covid-19 cases in Ghana. The study also shows that, the lowest number of Covid 19 related tweets were posted in December 2020, the month of the Ghanaian elections, whereas, the highest number of Covid 19 related tweets were posted in March 2020, the month in which the first case was detected in Ghana.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".