Using Twitter to investigate discourse on immigration: the role of values in expressing polarized attitudes toward asylum seekers during the closure of Roxham Road
Bibliographic record
Abstract
Introduction The world is witnessing an escalating migration crisis, and Canada, with its historically high immigration rates, is experiencing a rise in the number of asylum seekers entering the country as well. Despite generally positive Canadian attitudes toward newcomers, there is a notable division in opinions about welcoming them. Past studies suggest personal values significantly shape these attitudes, particularly conservation (resistance to change) and self-transcendence (concern for others). However, little research has examined if these values manifest in social media discussions about immigration, especially at times when policies change. This study examines how the discourse on immigration changes following the announcement of the closure of Roxham Road, a debated irregular border crossing between the US and Canada used by asylum seekers. Method In total, 33,459 Tweets referencing Roxham Road were collected over the course of 1 week (before, during, and after the closure). We used the Personal Values Dictionary to automatically assess references to personal values (i.e., conservation and self-transcendence) in the Tweets. Results The results indicate that expression of the values of conservation and self-transcendence were prevalent in discourse surrounding the closure of Roxham Road. Tweets expressing conservation had a negative tone, whereas Tweets expressing self-transcendence had a positive tone. Analyzing sentiment over time, Tweets reflecting conservation became less negative immediately after the closure, whereas Tweets reflecting self-transcendence values became more positive. Discussion The research highlights the interplay between personal values and policy change on immigration discourse and emphasizes the need for more analyses on how personal values are expressed in the public domain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".