Wildfire-Like Effect of a WhatsApp Campaign to Mobilize a Group of Predominantly Health Professionals With a University Degree on a Health Issue: Infodemiology Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Online interactions within a closed WhatsApp group can influence the attitudes and behaviors of the users in relation to health issues. OBJECTIVE This study aimed to analyze the activity of the members of a WhatsApp group initiated to raise awareness of the possible health effects of 5G mobile networks and mobilize members to sign the related petition. METHODS We retrospectively analyzed data from the WhatsApp group of 205 members that was active during 4 consecutive days in August 2019. The messages exchanged were collected, anonymized, and analyzed according to their timing and content. RESULTS The WhatsApp group members were invited to the group from the administrator’s contacts; 91% (187/205) had a university degree, 68% (140/205) were medical professionals, and 24% (50/205) held academic positions. Approximately a quarter of the members (47/205, 23%) declared in their messages they signed the corresponding petition. The intense message exchange had wildfire-like features, and the majority of messages (126/133, 95%) were exchanged during the first 26 hours. Despite the viral activity and high rate of members openly declaring that they signed the petition, only 8 (8/133, 6%) messages from the group members, excluding the administrator, referred to the health issue, which was the topic of the group. No member expressed an opposite opinion to those presented by the administrator, and there was no debate in the form of exchanging opposite opinions. CONCLUSIONS The wildfire-like activity of the WhatsApp group and open declaration of signing the petition as a result of the mobilization campaign were not accompanied by any form of a debate related to the corresponding health issue, although the group members were predominantly health professionals, with a quarter of holding academic positions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".