COVID‐19 pandemic and African innovation: Finding the good from the bad using Twitter data and text mining approach
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
This study investigates public sentiments and the essential topics of discussion on Africa's innovation amidst COVID-19. Web scraping techniques were used to collect and parse data from Twitter platform using the keywords "Africa Innovation COVID-19". A total of 54,318 cleaned English tweets were gathered and analysed using Twint Python Libraries. Our sentiment analysis findings revealed that 28,084 tweets (52 per cent) were positive, 21,037 (39 per cent), and 5197 (9 per cent) of tweets were neutral and negative, respectively, for Polarity sentiments. Notably, Healthcare, Imagination, Support, Webinar, Learning, Future, Rwanda, and Challenge were the most discussed topics on Africa's innovation during COVID-19. The topic labelling sentiments on the themes identified were positive, neutral, and negative, respectively. The study also revealed a cluster relationship between all identified topics. The relationship among these themes divulged how COVID-19 is positively shaping social and technological innovation in Africa. The study further presented practical implications to better position African leaders and policymakers to capitalise on the current innovation ecosystems and institutional capacities to transform the continent into a digital and innovation hub. The research concludes with theoretical recommendations and study limitations that will guide researchers and academicians in conducting future research in the subject area.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it