Social media, migration and the platformization of moral panic: Evidence from Canada
Bibliographic record
Abstract
As a contentious issue affecting the character, boundaries and future of social order, migration represents a recurrent source of moral panic. While analysts have considered conventional outlets’ role in triggering collective alarm, less is known about social media’s effects on migration’s construction as a social problem. Working with an original dataset of tweets from the 2019 Canadian election, a period of heightened concern and outcry for significant portions of the electorate, this paper employs content analytic methods to assess migration’s online demonization and interrogate the patterns of framing, participation and engagement brought within the issue’s orbit. Alongside documenting significant disquiet and antipathy, its findings suggest that Twitter is transforming panic production and facilitating forms of reaction involving mass-participation and collaboration; interference from automated ‘bots’ and considerable dispute, dissent and negotiation. Based on these results, the sensitizing concept of platformed panics is proposed to capture how social media’s technical affordances, design and appropriation align to promote moral panics that are networked, algorithmic and contested.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".