The role of social media in delivering news related to the COVID-19 pandemic: Moroccan community as a case study
Bibliographic record
Abstract
The study aims to assess the Moroccan community’s using rate of social media, especially during the imposed lockdown, and analyze how the community is using and exploring the news published on Facebook. In this order, we prepared and shared a survey questionnaire among Facebook, Twitter and WhatsApp users. The obtained responses exhibit that only 5% of respondents share the news immediately without verifying the source, while 54 % share news only after verifying the source; the rest did not prefer to share COVID-19 related news. This may reflect the awareness level of the sampled population about the importance of verifying the source of information before sharing it, especially during such conditions. However, 64% of participants think that Social Media platforms are not sufficient and appropriate to warn and inform the population about this sanitary crisis as not all Moroccan citizens have access to the internet and do not use social media. Besides, the COVID-19 period has known a rapid spread of misinformation and fake news through these platforms, impacting community mental health. Although, it is recommended to consider warning people about the best practices and use of shared information through these platforms
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".