Unveiling the Digital Underworld – Exploring Cyberbanging and Recruitment of Canadian Street Gang Members on Social Media
Bibliographic record
Abstract
This study explores the online behavior of Canadian street gang members on X (formerly known as “Twitter”), Facebook, and YouTube. As scholarly Western inquiries have predominantly focused on American and British street gangs, their presentation of online recruitment often differs. Law enforcement and journalists claim that street gangs have begun using social media platforms explicitly to search for and recruit new members. Certain scholarly publications have also suggested that a small minority of street gangs utilize social media to recruit new participants; however, they do not provide ample context to describe what this looks like online. Others argue that while cyberbanging – online gang content and propaganda – can be found online, some of this content can act as indirect recruitment, as it promotes gang lifestyles to the outside observer. To examine the use of cyberbanging and the indirect recruitment it inspires, the present study examines 59 social media user profiles linked to Canadian street gang members (23 Twitter users and 36 Facebook users), along with 10 YouTube rap videos produced by street gang members, to assess the online behavior of these particular social media users. The results suggest that the most prominent type of cyberbanging content is the promotion of gang ideologies, with a clear presence of indirect recruitment techniques observed, such as displaying drugs, weapons, money, and illegal gains, boasting of facets of gang lifestyles, and other propaganda. This content, while considered cyberbanging, blurs the line between content defined as cyberbanging and that of online recruitment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".