Canadian politicians' rhetoric on Twitter/X: Analysing prejudice and inclusion towards Muslims using structural topic modelling and rhetorical analysis
Bibliographic record
Abstract
We analysed tweets from five English-speaking Canadian political parties in the year leading up to the 2019 federal election to explore both prejudicial and inclusive rhetoric in relation to Muslim identities on social media. We used structural topic modelling to understand what topics were discussed before moving to a rhetorical approach to analyse how topics were discussed. We identified 10 topics. Seven talked about Muslim groups in primarily inclusive ways, including depicting the positive contributions to Canadian society, creating ideological space for Muslim religious practices and invoking superordinate identities with victims of hate crimes to cultivate solidarity. However, the effectiveness of inclusive rhetoric was sometimes questioned due to omitting the subgroup-specific prejudice faced by Muslims. Prejudicial rhetoric occurred in three of the topics due to the nativist populist PPC party depicting Muslims as a threat to Canadian values, as hostile to people from other religious faiths, and depicting 'elites' in society as concealing the 'true' information concerning Muslims. The study contributes to understanding how politicians attempt to cultivate minority inclusion/exclusion in multicultural contexts through social media, as well as understanding the rhetoric of nativist populism in Canada and its similarities to other Global North contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".