Fear of the dark: a cross-cultural study into how perceptions of antisocial behaviour impact the acceptance and use of Twitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the impact of the perceptions of antisocial behaviour on the use of the social media platform Twitter. We extend the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Perception of Antisocial Behaviour as a risk factor, and two supporting constructs: Strategic Self-Presentation and Protective Self-Presentation. We call this extended model Technology Acceptance and Use under Risk (TAUR). We investigate two groups via an online questionnaire, contrasting Anglophone countries (the UK, USA, and Canada, 200 responses), with Saudi Arabia (540 responses). In both cases the data shows that the Perception of Antisocial Behaviour impacts Twitter use, but not directly, rather it negatively impacts the influence of other factors such as Behavioural Intention – it also shows that this affects Anglophones more than Saudis. This indicates that future work should differentiate between different cultural groups, and different solutions may be needed to assuage users’ fears in different parts of the world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it