COVID‐19, media coverage of bats and related Web searches: a turning point for bat conservation?
Bibliographic record
Abstract
SARS-CoV-2, the virus that caused the COVID-19 pandemic, is genomically similar to a SARS-like beta-coronavirus found in Asian rhinolophid bats. This evolutionary relationship impressed the global media, which then emphasised bats as key actors in the spillover that resulted in the pandemic. In this study, we highlight changes in the traditional and new media coverage of bats and in Internet search volumes that occurred since the beginning of the COVID-19 pandemic in 2020.We analysed Google and Wikipedia searches for bats and coronaviruses in 21 countries and eight languages, as well as television broadcasts in the USA, some of which have global coverage, between January 2016 and December 2020. In January 2020, the amount of television news about bats boomed, and news associated with the term 'bat' shifted to COVID-19-related topics. A nearly identical pattern was observed in Google searches during 2020 at the global scale. The daily time series of television coverage and Internet search volumes on bats and coronavirus in the USA covaried in the first quarter of 2020, in line with the existence of a media bubble. Time-series analysis revealed that both the Google Trends index and visits to Wikipedia pages about bats boomed in early 2020, despite the fact that this time of year is usually characterised by low search volumes.Media coverage emphasised, correctly or not, the role of bats in the COVID-19 pandemic and amplified public interest in bats worldwide. The public image of these mammals, in many cases threatened and important ecosystem service providers, was seriously compromised. We therefore recommend that policymakers and journalists prioritise scientifically accurate communication campaigns about bats, which would help counteract the surge in bat persecution, and leverage interest towards positive human-bat interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".