Functions, Influences & Effects of WhatsApp Use During the Movement Control Order (MCO) in Malaysia
Bibliographic record
Abstract
On March 18, 2020, the Malaysian government took a firm position to halt the spread of the COVID-19 pandemic by putting in effect the Movement Control Order (MCO). By that time, Malaysia had recorded deaths and the number of infections was hundreds. During this period, in addition to the use of popular social media platforms such as Facebook and Twitter for rapid information communication, the WhatsApp messaging app was also heavily relied upon during the MCO. In addition to providing information, WhatsApp was also considered to play an important role in daily tasks as well as in education. This article discusses the functions, influences and effects of the use of WhatsApp among Malaysians during the MCO. This research conducted a structured interview with 10 informants from diverse backgrounds and age range. The data was then transcribed verbatim. Analysis of the results revealed that WhatsApp's main functions were to facilitate communication with family members and employers, as well as the means for a rapid exchange of information. On the other hand, the informants revealed that some information shared in WhatsApp was unreliable since there were irresponsible people who were creating and sharing fake news. The informants were also aware that the dissemination of fake news will cause mass panic among the Malaysians. As such, the informants would refer to reliable sources to determine the authenticity of the news they have encountered. This action reflected a mature attitude using WhatsApp during the MCO.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".