Break the Hate: A Survey of Youth Experiences with Hate and Violent Extremism Online
Bibliographic record
Abstract
As the most prolific users of the Internet, youth are exposed to a diverse array of harmful content and experiences, including cyberbullying and sexual exploitation. What is less well understood is the impact of hate and violent extremism on youth in these online spaces. This study surveyed over 800 youth from Alberta, Canada, to identify where they most frequently encountered hateful and extremist content online, how they react to it, and what they believed were the most appropriate responses to these problems. This study adds to a growing literature which takes youth perspectives seriously in the study of this problem. Our study found that more than three-quarters of youth surveyed reported encountering hateful content, while more than two-thirds reported encountering extremist content. Our findings add to a growing debate on the relationship between identity factors and exposure. While our results indicate respondents who identify as female are more likely to report encountering extremist and hateful content than males, intersectionality factors shed new light on the patterns of online exposure among youth. Specifically, we found that the effect of gender is mediated by other identity factors, like being a visible minority or identifying as 2SLGBTQ+.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".