Exotic pet trade in Canada: The influence of social media on public sentiment and behaviour
Bibliographic record
Abstract
The live trade in wild animals can increase the risk of escape of exotic animals, introduce invasive species, spread zoonotic diseases, over-exploit wild populations, and harm animal welfare. Trade in exotic pets is a particularly understudied issue in Canada. While Canadians generally have pro-environmental attitudes, it is unclear whether this extends to the trade in exotic animals. With most Canadians on social media, we aimed to use Natural Language Processing of social data to examine public sentiment towards exotic pet trade in Canada. We analysed 9,274 posts on Twitter (now 'X') about exotic pets between 2012 and 2022, and 150,236 comments from 2568 TikTok videos showing exotic pets from 50 unique Canadian accounts. We found that social media users demonstrate markedly positive attitudes towards the live trade in reptiles and amphibians, mammals, birds, and arachnids and insects, even on TikTok videos showing poor animal care and questionable legality. We propose a conceptual framework for how exotic pet influencers directly and indirectly contribute to increased demand for exotic pets through opinion leadership, sharing information on where to buy exotic pets, and normalising exotic pet ownership. We suggest that it is important to raise public awareness among social media users about the challenges associated with wildlife trade, including animal welfare considerations, and the links between exotic pet trade and conservation.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".