Social media filtering of sensationalistic news on spiders—A global overview
Bibliographic record
Abstract
Abstract The interplay between traditional and social media is a critical aspect of information dissemination. Acting as news filters, social media platforms can amplify the visibility of specific content and shape emotions towards wildlife. Widely feared animals (e.g. spiders, snakes, large carnivores) often become the unfortunate antagonists of sensationalistic and inaccurate news articles that exploit biophobic sentiments, which can be further magnified by social media. Two questions arise: To what extent does this pattern extend globally, and what are the factors involved in driving the sharing of spider‐related articles on social media? To answer these questions, we used a global database of 5348 spider‐related news articles published between 2010 and 2020, covering 81 countries and 40 languages. For each article, the database contains information on the reported human–spider encounter and a quantitative characterization of the content, including number and type of errors, consultation with experts and a qualitative assessment of sensationalism. We used regression models to test the impact of eight article‐level features on the sharing of newspaper articles on social media (Facebook). Our analysis reveals that social media sharing is primarily driven by sensationalism and a focus on potentially deadly species and is independent of whether articles contain factual errors. Importantly, social media sharing showed a highly left‐skewed distribution, with 53% of articles never being shared. These articles tended to focus on harmless spider species, lacked visuals and were published in local news outlets. Understanding the role of social media in filtering traditional news articles is key to mitigating the spread of fear and reducing human–wildlife conflicts. By promoting active collaboration among scientists, science communicators and journalists and educating people to fact‐check spider‐related content, we envision a positive transformation in the news shared on social media, which would consequently improve public perception and understanding of spiders. This is crucial, given that biophobias may be rising in contemporary societies, incurring high socio‐economic costs. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".