From the Streets to the Tweets: Social Network Analysis of Canadian Street Gang Members and Their Use of Twitter, Facebook and Youtube
Bibliographic record
Abstract
In the last two decades, the use of social networking sites (SNS) has increased at an astounding rate. Research regarding street gangs’ use of social media generally suggests two assumptions: Firstly, street gang members have infiltrated SNS, and have transferred much of their gang culture from the real world to the virtual one—a process known as cyber-banging. Secondly, street gangs have begun using SNS to recruit potential members; a fear propagated by newspapers and police forces. While cyber-banging has be well documented, the same cannot be said about online recruitment. This study achieved two goals: firstly, using a similar keyword search to that which was utilised by Morselli & Décary-Hétu (2013) this study empirically illustrates the prevalence of cyber-banging and recruitment on SNS. To accomplish this goal, this study sampled 23 Twitter users and 36 Facebook users flagged as street gang members across Canada, along with 10 YouTube rap videos created by gang members. Secondly, using a network analysis add-on to Microsoft Excel called NodeXL, this essay employed social network analysis to test whether centrality measures can extrapolate gang roles within a gang member’s Twitter network. Using content analysis, the results demonstrated that the most prominent type of cyber-banging is content that promotes gang ideologies. The results also conclude that none of the content on Facebook, Twitter, and YouTube can be considered proactive recruitment techniques. Regarding the second goal, using a combination of degree centrality, betweenness centrality and eigenvector centrality measures, results suggest many of the gang members are central in their network, but probably not gang leaders; while a look at the relationship between activity levels and number of followers on Twitter demonstrate four possible roles a SNS user has within his gang.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".