An expert-curated global database of online newspaper articles on spiders and spider bites
Bibliographic record
Abstract
Mass media plays an important role in the construction and circulation of risk perception associated with animals. Widely feared groups such as spiders frequently end up in the spotlight of traditional and social media. We compiled an expert-curated global database on the online newspaper coverage of human-spider encounters over the past ten years (2010-2020). This database includes information about the location of each human-spider encounter reported in the news article and a quantitative characterisation of the content-location, presence of photographs of spiders and bites, number and type of errors, consultation of experts, and a subjective assessment of sensationalism. In total, we collected 5348 unique news articles from 81 countries in 40 languages. The database refers to 211 identified and unidentified spider species and 2644 unique human-spider encounters (1121 bites and 147 as deadly bites). To facilitate data reuse, we explain the main caveats that need to be made when analysing this database and discuss research ideas and questions that can be explored with it.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".