Consolidate metadata and add paper/project links

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the dataset card by:
- Consolidating the YAML metadata into a single block at the top.
- Setting the correct task category to `other`.
- Adding links to the research paper [Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment](https://huggingface.co/papers/2606.02946) and the related [Live or Lie](https://arxiv.org/abs/2602.03520) work.
- Including the project page and GitHub repository for better discoverability.
- Cleaning up redundant headers and duplicated sections in the Markdown content.
- Maintaining the technical instructions for LMDB reconstruction and data loading.

README.md

@@ -1,40 +1,32 @@
 ---
-license: mit
----
----
-pretty_name: "Live or Lie — Live Streaming Room Risk Assessment (May/June 2025)"
+license: cc-by-4.0
+task_categories:
+- other
 language:
-  - zh
+- 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
-license: other
+- live-streaming
+- risk-assessment
+- fraud-detection
+- weak-supervision
+- multiple-instance-learning
+- behavior-sequence
 ---
 
-# Dataset Card: Live Streaming Room Risk Assessment (May/June 2025)
+# Live or Lie — Live Streaming Room Risk Assessment (May/June 2025)
 
----
-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
----
+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
-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)**.
+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.
+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.
@@ -57,16 +49,10 @@ The dataset is organized into two time-indexed subsets (May and June). Large LMD
 ├── 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**.
-
+- 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**:
@@ -81,13 +67,12 @@ Each room has a label and a sequence of **actions**:
   - `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
+  - `user_id` (`string`): user identifier 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.
+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
-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 |
@@ -99,11 +84,10 @@ The paper uses two datasets (“May” and “June”), each with train/val/test
 
 ## 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.
+### 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
@@ -115,17 +99,16 @@ 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
-```
+### 2. Loading Data
+Install the Python package: `pip 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
+# Example: read a specific room
+room_id = 0 
+lmdb_path = "final_May_hard1_masked_encoded.lmdb"
 
 env = lmdb.open(
     lmdb_path,
@@ -139,41 +122,39 @@ 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)
+        patch_list = data["patch_list"] # list of tuples
         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 |
-
----
+## 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 (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. 
+### Intended Use 
+- Research on weakly-supervised risk detection / MIL in live streaming.
+- Early-warning room-level moderation signals.
+- Interpretability over localized behavior segments.
 
-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.
+### 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/
+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}
+}
+```