ByteDance/LiveStreamingRiskControl

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

Dataset Card: Live Streaming Room Risk Assessment (May/June 2025)

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

Dataset Summary

This dataset contains live-streaming room interaction logs for room-level risk assessment under weak supervision. 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.

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.

.
├── 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

## Dataset Summary
This dataset contains **live-streaming room interaction logs** for **room-level risk assessment** under **weak supervision**. 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.


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


## 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 indentifier across rooms

## Action Space
The paper’s 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
The paper uses two datasets (“May” and “June”), each with train/val/test 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. Run the following commands in your terminal:
Below we provide a simple example showing how to load the dataset.

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. We use LMDB to store and organize the data. Please install the Python package first:

pip3 install lmdb

Here is a minimal demo for reading an LMDB record:
```python
import lmdb
import pickle

room_id = 0  # the room you want to read

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: (u_idx, t, l, action_id, action_vec, timestamp, action_desc, global_user_idx)
        room_label = data["label"]

# close lmdb after reading
env.close()

Claim

To ensure the security and privacy of TikTok 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. As a result, some fine-grained behavioral signals are inevitably lost, which leads to a performance drop for AC-MIL. The corresponding results are shown below.

Split	PR_AUC	ROC_AUC	R@P=0.9	P@R=0.9	R@FPR=0.1	FPR@R=0.9
May	0.6518	0.9034	0.2281	0.2189	0.7527	0.3215
June	0.6120	0.8856	0.1685	0.1863	0.7111	0.3935

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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 (capsule-level evidence)

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, Limitations, and Recommendations
• 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.
• Weak supervision: only room-level labels are provided; interpretability at capsule level is model-derived.

License

This dataset is licensed under CC BY 4.0: https://creativecommons.org/licenses/by/4.0/