Digital Islamophobia: A Comparison of Right-wing Extremist Groups in Canada, the US, and India
Bibliographic record
Abstract
Despite recommendations for a global or transnational focus on the study of right-wing extremism, transnational comparative analyses of right-wing extremists remain relatively uncommon. While extant transnational studies of Islamophobia examine the phenomenon among countries in the global North, comparisons between countries in the global North and South are also still quite rare. Thus, my research responds to these knowledge gaps by examining how right-wing extremists in Canada, the US, and India exploit and weaponize gendered, affective, and emotional rhetoric and related visual content through Twitter to perpetuate Islamophobia. Building upon my analysis of digital media and governmentality, my research evidences how the digital architecture of Twitter facilitates and shapes important political relationships and affective alignments between these seemingly unrelated and geographically distant groups. To the casual observer, Hindu nationalism and white supremacy are disparate phenomena, however, as I show, the similarities in how Muslims are negatively constructed by these groups are quite striking. As I show throughout my dissertation, the transnational quality of Islamophobia in, across, and between right-wing extremist groups (RWEGs) is as much about looking in on particular groups as it is about looking outside to their mutually held investments which manifest and co-create connections and affective alignments between such extremist groups and movements in and between the global South and the global North. Thus, my dissertation compares the Twitter usage of Islamophobic right-wing extremists, focusing on anti-Muslim rhetoric in Hindu nationalist and white supremacist discourses. Using a transnational, intersectional, and affective feminist media framework, my study demonstrates the similarities, differences, and interconnections of digital Islamophobia in these three countries. Across this project, I pay specific attention to the role of digital platform politics, technical, architectural affordances, and affectively-charged and highly gendered rhetoric exploited by these groups. Ultimately, my research evidences the emergence of a digital, transnational, and affective alignment between such groups in their mutual pursuit of anti-Muslim sentiment.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".