Privacy and Safety on Social Networking Sites: Autistic and Non-Autistic Teenagers’ Attitudes and Behaviors
Bibliographic record
Abstract
Autistic teenagers are suspected to be more vulnerable to privacy and safety threats on social networking sites (SNS) than the general population. However, there are no studies comparing these users’ privacy and safety concerns and protective strategies online with those reported by non-autistic teenagers. Furthermore, researchers have yet to identify possible explanations for autistic teenagers’ increased risk of online harms. To address these research gaps, we conducted semi-structured interviews with 12 autistic and 16 non-autistic teenagers assessing their privacy- and safety-related attitudes and behaviors on SNS, and factors affecting them. We used videos demonstrating relevant SNS scenarios as prompts to engage participants in conversation. Through our thematic analyses, we found evidence that autistic teenagers may be more averse to taking risks on SNS than non-autistic teenagers. Yet, several personal, social, and SNS design factors may make autistic teenagers more vulnerable to cyberbullying and social exclusion online. We provide recommendations for making SNS safer for autistic teenagers. Our research highlights the need for more inclusive usable privacy and security research with this population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".