README.md

---
license: cc-by-4.0
task_categories:
- other
language:
- zh
pretty_name: "Live or Lie — Live Streaming Room Risk Assessment (May/June 2025)"
tags:
- live-streaming
- risk-assessment
- fraud-detection
- weak-supervision
- multiple-instance-learning
- behavior-sequence
---

# Live or Lie — Live Streaming Room Risk Assessment (May/June 2025)

This dataset contains live-streaming room interaction logs for room-level risk assessment under weak supervision. It is the official dataset for the research presented in the papers:
- **Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment** ([Hugging Face Papers](https://huggingface.co/papers/2606.02946))
- **Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platforms** ([arXiv](https://arxiv.org/abs/2602.03520))

**Project Page:** [https://qiaoyran.github.io/LiveStreamingRiskAssessment/](https://qiaoyran.github.io/LiveStreamingRiskAssessment/)
**GitHub:** [https://github.com/bytedance/AC-MIL](https://github.com/bytedance/AC-MIL)

## Dataset Summary
Each example corresponds to a single live-streaming room and is labeled as **risky (> 0)** or **normal (= 0)**. 

The task is designed for early detection: each room’s action sequence is **truncated to the first 30 minutes**, and can be structured into **user–timeslot capsules** for models such as AC-MIL or LPCD.

## File Structure
The dataset is organized into two time-indexed subsets (May and June). Large LMDB data files are provided in multiple `.part` chunks to comply with storage limits.

```text
.
├── final_May_hard1_masked_encoded.lmdb/
│   ├── data.mdb.00.part
│   ├── data.mdb.01.part
│   ├── data.mdb.02.part
│   ├── data.mdb.03.part
│   └── lock.mdb
├── final_June_hard1_masked_encoded.lmdb/
│   ├── data.mdb.00.part
│   ├── data.mdb.01.part
│   └── lock.mdb
├── May_train.csv
├── May_val.csv
├── May_test.csv
├── June_train.csv
├── June_val.csv
└── June_test.csv
```

## Languages
- Predominantly **Chinese (zh)**: user behaviors are presented in Chinese, e.g., "主播口播：...". These action descriptions are encoded as action vectors via a **Chinese-BERT** model.

## Data Structure
Each room has a label and a sequence of **actions**:

- `room_id` (`string`)
- `label` (`int32`, {0,1,2,3}))
- `patch_list` (`list` of tuples):
  - `u_idx` (`string`): user identifier within a room
  - `t` (`int32`): time index along the room timeline
  - `l` (`int32`): capsule index
  - `action_id` (`int32`): action type ID 
  - `action_vec` (`list<float16>` or `null`): action features encoded from masked action descriptions
  - `timestamp` (`string`): action timestamp
  - `action_desc` (`string`): textual action descriptions
  - `user_id` (`string`): user identifier across rooms

## Action Space
The setup includes both viewer interactions (e.g., room entry, comments, likes, gifts, shares, etc.) and streamer activities (e.g., start stream, speech transcripts via voice-to-text, OCR-based visual content monitoring). Text-like fields are discretized as part of platform inspection/sampling.

## Data Splits
| Split | #Rooms | Avg. actions | Avg. users | Avg. time (min) |
|------:|------:|-------------:|-----------:|----------------:|
| May train | 176,354 | 709 | 35 | 30.0 |
| May val   | 23,859  | 704 | 36 | 29.6 |
| May test  | 22,804  | 740 | 37 | 29.7 |
| June train| 80,472  | 700 | 36 | 30.0 |
| June val  | 10,934  | 767 | 40 | 29.1 |
| June test | 11,116  | 725 | 37 | 29.1 |

## Quickstart

### 1. Reconstruct the LMDB files
Before loading the data, you must merge the split parts back into a single `data.mdb` file for each subset.

```bash
# Reconstruct May Dataset
cd final_May_hard1_masked_encoded.lmdb
cat data.mdb.*.part > data.mdb
cd ..

# Reconstruct June Dataset
cd final_June_hard1_masked_encoded.lmdb
cat data.mdb.*.part > data.mdb
cd ..
```

### 2. Loading Data
Install the Python package: `pip install lmdb`

```python
import lmdb
import pickle

# Example: read a specific room
room_id = 0 
lmdb_path = "final_May_hard1_masked_encoded.lmdb"

env = lmdb.open(
    lmdb_path,
    readonly=True,
    lock=False,
    map_size=240 * 1024 * 1024 * 1024,
    readahead=False,
)

with env.begin() as txn:
    value = txn.get(str(room_id).encode())
    if value is not None:
        data = pickle.loads(value)
        patch_list = data["patch_list"] # list of tuples
        room_label = data["label"]

env.close()
```

## Security and Privacy
To ensure the security and privacy of users, all data collected from live rooms has been anonymized and masked, preventing any content from being linked to a specific individual. In addition, action vectors are re-encoded from the masked action descriptions.

## Considerations for Using the Data

### Intended Use 
- Research on weakly-supervised risk detection / MIL in live streaming.
- Early-warning room-level moderation signals.
- Interpretability over localized behavior segments.

### Out-of-scope / Misuse 
- Do not use this dataset to identify, profile, or target individuals. 
- Do not treat predictions as definitive enforcement decisions without human review. 

### Bias and Limitations 
- Sampling bias: negatives are downsampled (1:10); reported metrics and thresholds should account for this. 
- Domain specificity: behavior patterns are platform- and policy-specific; transfer to other platforms may be limited.

## License
This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

## Citation
```bibtex
@article{qiao2026live,
  title={Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platforms},
  author={Qiao, Yiran and Chen, Jing and Ao, Xiang and Zhong, Qiwei and Liu, Yang and He, Qing},
  journal={arXiv preprint arXiv:2602.03520},
  year={2026}
}
```