The searchscape of fear: A global analysis of internet search trends for biophobias
Bibliographic record
Abstract
Abstract Human relationships with nature may sometimes manifest through fear, disgust and other disease‐avoidance mechanisms. While there is an evolutionary utility to these so‐called ‘biophobias’, many people exhibit phobic responses towards organisms that pose no tangible threats, potentially leading to excessive anxiety and avoidance of interactions with nature. Understanding the drivers of the prevalence and spread of biophobias in modern societies is, therefore, a growing concern. Here, we posit that online information‐seeking patterns may reveal general insights into biophobias. Using a culturomics approach, we gathered temporal (2004–2022) and country‐level data on the volume of internet searches for 25 biophobias, as well as 25 general phobias acting as a benchmark group. We explored temporal trends in the volume of search for each biophobias and modelled relationships between search volume for biophobias and five country‐level variables. We observed a steady increase in online search volume for biophobias between 2004 and 2022. Yet, there were marked differences in individual trends, with 17 biophobias showing positive, three negative and five stationary temporal trends. Arachnophobia (fear of spiders) attracted the most interest, followed by mysophobia (fear of microbes) and parasitophobia (fear of parasites). The United States, the United Kingdom, Australia, Canada, Mexico and India recorded wide interest in most biophobias, whereas 49% of countries showed no search volume for any biophobia. Search patterns for biophobias were strongly associated with the percentage of urban population, urban population growth and the number of extant venomous species in a given country. Conversely, search patterns for biophobias were weakly correlated with the incidence of anxiety disorders in a country's population and the likelihood of encounters with venomous animals. Our results provide quantitative support to the hypothesis that biophobias are broadly prevalent and possibly increasing as a result of widespread urbanisation and loss of experiences with nature. We suggest that people affected by biophobic disorders may be using the Internet as a key venue to seek relevant information to appraise their condition and identify coping mechanisms. These findings have broad ramifications for understanding and mitigating human–wildlife conflicts and the prevalence of widespread biophobic sentiments in modern societies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".