Duplicate from ByteDance/LiveStreamingRiskControl

Co-authored-by: Yiran Qiao <yrqiao@users.noreply.huggingface.co>

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README.md

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+---
+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}
+}
+```

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