Datasets:
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pretty_name: MDU-RiskText and MDU-RiskBench
language:
- en
- zh
license: other
task_categories:
- text-classification
tags:
- privacy
- video-to-text
- ordinal-classification
- youth-digital-use
- evidence-grounding
configs:
- config_name: video_only
data_files:
- split: train
path: MDU-RiskText/video_only/train.jsonl.gz
- split: validation
path: MDU-RiskText/video_only/validation.jsonl.gz
- split: test
path: MDU-RiskText/video_only/test.jsonl.gz
- config_name: non_video_only
data_files:
- split: train
path: MDU-RiskText/non_video_only/train.jsonl.gz
- split: validation
path: MDU-RiskText/non_video_only/validation.jsonl.gz
- split: test
path: MDU-RiskText/non_video_only/test.jsonl.gz
- config_name: all_filtered_text
data_files:
- split: train
path: MDU-RiskText/all_filtered_text/train.jsonl.gz
- split: validation
path: MDU-RiskText/all_filtered_text/validation.jsonl.gz
- split: test
path: MDU-RiskText/all_filtered_text/test.jsonl.gz
---
# MDU-RiskText and MDU-RiskBench
This repository contains the public 850-participant release associated with
PriVTE: Privacy-Preserving Video-to-Text Evidence Encoding for Youth Digital
Use Risk Screening.
## Data products
- **MDU-RiskText** contains participant-level, privacy-filtered textual
evidence, the frozen ordinal target, and participant-disjoint split for all
850 participants.
- **MDU-RiskBench** defines the task, label order, three headline evidence
settings, participant index, evaluation protocol, and reference metric code.
The frozen cohort contains 9,831 selected source videos. It is split into 593
train, 131 validation, and 126 test participants. Target counts are 226
`no_observed_risk`, 432 `mild_risk`, 172 `moderate_risk`, and 20 `high_risk`.
## Load with Hugging Face Datasets
```python
from datasets import load_dataset
video = load_dataset("Herrieson/MDU-RiskText", "video_only")
non_video = load_dataset("Herrieson/MDU-RiskText", "non_video_only")
combined = load_dataset("Herrieson/MDU-RiskText", "all_filtered_text")
```
Replace `Herrieson/MDU-RiskText` if the final dataset repository uses a
different owner or name. Each row includes `sample_id`, `split`,
`target_label`, `benchmark_setting`, `llm_evidence_package`, and
`text_evidence`. The last field is the compact JSON string supplied to the
text-only model runners.
## Headline settings
| Configuration | Model-facing evidence |
|---|---|
| `video_only` | PriVTE evidence derived from locally processed video |
| `non_video_only` | Coarsened app-category, heart-rate-bin, and questionnaire-risk text |
| `all_filtered_text` | PriVTE video evidence plus the coarsened auxiliary text |
## Labels and evaluation
The four risk labels are ordinal in the order listed above.
`insufficient_evidence` is reserved for model abstention and is not a fifth
severity. The benchmark reports exact accuracy and macro-F1, together with
ordinal MAE and RMSE on non-abstained predictions and prediction coverage.
## Data construction
The source cohort was collected in school-based field studies involving young
participants in primary, middle, and high school settings in Beijing,
Zhejiang, Inner Mongolia, and other regions. Participants completed an
approximately 45-minute tablet-use session. PriVTE processes source video
locally and emits ordered observable-behavior evidence with quality gates,
relative stages, and evidence references. Auxiliary inputs are released only
as coarse categories or risk-signal bins.
Each participant contributes at most 12 videos selected uniformly over source
order. Every selected file was required to be nonempty, parseable, contain a
video stream of known duration, and provide at least four seconds of video.
The selection protocol does not replace a failed selected file with a nearby
file.
## Public record design
Public sample IDs (`MDURT_######`) are consistent across all three settings.
The release records contain the complete model-facing evidence used by
MDU-RiskBench, but omit redundant internal preprocessing/debug copies and
operational provenance fields. `MANIFEST.json` reports row counts, byte sizes,
and SHA-256 hashes for every release artifact.
## Intended use
The resource supports research on privacy-preserving behavioral evidence,
text-only ordinal screening, evidence grounding, selective prediction, and
comparison with direct-video systems. The targets are field-derived screening
judgments rather than clinical diagnoses. Model outputs are intended for
research analysis and human review rather than automated punitive decisions.
## License and citation
See `LICENSE_DATA.md` for the dataset terms and `CITATION.cff` for citation
metadata. Software scripts are released under the accompanying code license;
the data terms apply to MDU-RiskText and MDU-RiskBench records.
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