Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- vi
+tags:
+- tiktok
+- video
+- multimodal
+- harmful-content
+- content-moderation
+pretty_name: TikTok Harmful Video Dataset (Vietnamese)
+task_categories:
+- video-classification
+---
+
+# Dataset Card for TikTok Harmful Video Dataset (Vietnamese)
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset contains TikTok videos collected for research on harmful content detection in Vietnamese.
+Each sample is stored as a folder with:
+- the original video file (`video.mp4`)
+- a metadata file (`metadata.json`)
+
+The dataset is designed for multimodal learning (video + audio + text from metadata).
+
+- **Curated by:** Student research project (IE212 – Big Data, UIT, VNU-HCM)
+- **Language(s):** Vietnamese
+- **License:** CC BY-NC 4.0 (non-commercial research/education use)
+
+### Dataset Sources
+
+- **Source platform:** TikTok
+- **Collection method:** Automated crawling (keyword/hashtag-based), then manual filtering
+
+## Uses
+
+### Direct Use
+
+You can use this dataset for:
+- Video classification (e.g., Safe vs Not Safe)
+- Multimodal research (video/audio/text)
+- Feature extraction pipelines (frames, audio waveform, captions)
+
+### Out-of-Scope Use
+
+This dataset is **not** intended for:
+- Commercial use
+- Decisions impacting individuals (e.g., banning accounts, legal enforcement)
+- Identifying or profiling TikTok users
+
+## Dataset Structure
+
+Data is stored using a simple folder-per-video layout:
+
+```
+{video_id}/
+├── video.mp4
+└── metadata.json
+```
+
+**Files**
+- `video.mp4`: raw TikTok video
+- `metadata.json`: JSON describing the video (caption/hashtags/stats/etc., depending on what your crawler saved)
+
+> Note: This dataset does not necessarily include official train/val/test splits.
+
+## How to Use (Python)
+
+Below are simple examples to load and iterate the dataset from a local folder clone.
+
+### 1. List samples and read metadata
+
+```python
+import json
+from pathlib import Path
+
+dataset_dir = Path("PATH_TO_DATASET_ROOT")  # e.g. "./tiktok_dataset"
+
+video_folders = [p for p in dataset_dir.iterdir() if p.is_dir()]
+print("Total samples:", len(video_folders))
+
+# Read first sample
+sample_dir = video_folders[0]
+video_path = sample_dir / "video.mp4"
+meta_path = sample_dir / "metadata.json"
+
+with meta_path.open("r", encoding="utf-8") as f:
+    meta = json.load(f)
+
+print("Sample video_id:", sample_dir.name)
+print("Video path:", video_path)
+print("Metadata keys:", list(meta.keys()))
+```
+
+### 2. Build a simple manifest (CSV/JSONL) for training
+
+This is useful if you want a single file listing all samples.
+
+```python
+import json
+import csv
+from pathlib import Path
+
+dataset_dir = Path("PATH_TO_DATASET_ROOT")
+out_csv = Path("manifest.csv")
+
+rows = []
+for d in dataset_dir.iterdir():
+    if not d.is_dir():
+        continue
+    video_path = d / "video.mp4"
+    meta_path = d / "metadata.json"
+    if not video_path.exists() or not meta_path.exists():
+        continue
+
+    meta = json.loads(meta_path.read_text(encoding="utf-8"))
+    caption = meta.get("caption") or meta.get("desc") or ""
+
+    # If you have labels inside metadata.json, try:
+    # label = meta.get("label")  # e.g. "safe" / "not_safe"
+    # Otherwise set it to empty and label later.
+    label = meta.get("label", "")
+
+    rows.append({
+        "video_id": d.name,
+        "video_path": str(video_path),
+        "caption": caption,
+        "label": label,
+    })
+
+with out_csv.open("w", newline="", encoding="utf-8") as f:
+    writer = csv.DictWriter(f, fieldnames=["video_id", "video_path", "caption", "label"])
+    writer.writeheader()
+    writer.writerows(rows)
+
+print("Wrote:", out_csv, "rows =", len(rows))
+```
+
+### 3. Extract audio from MP4 (optional)
+
+If you want audio for ASR or audio embeddings, you can extract WAV using `ffmpeg`.
+
+```python
+import subprocess
+from pathlib import Path
+
+video_path = Path("PATH_TO_A_VIDEO.mp4")
+out_wav = video_path.with_suffix(".wav")
+
+cmd = [
+    "ffmpeg", "-y",
+    "-i", str(video_path),
+    "-ac", "1",          # mono
+    "-ar", "16000",      # 16kHz
+    str(out_wav)
+]
+subprocess.run(cmd, check=True)
+print("Saved:", out_wav)
+```
+
+### 4) Read frames with OpenCV (optional)
+
+```python
+import cv2
+
+video_path = "PATH_TO_A_VIDEO.mp4"
+cap = cv2.VideoCapture(video_path)
+
+frames = []
+max_frames = 16
+
+while len(frames) < max_frames:
+    ok, frame = cap.read()
+    if not ok:
+        break
+    frames.append(frame)
+
+cap.release()
+print("Extracted frames:", len(frames))
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to support Vietnamese-focused research on harmful content detection for short-form videos.
+It supports multimodal modeling by combining raw video with metadata text.
+
+### Data Collection and Processing
+
+* Videos were collected using keyword/hashtag-based crawling.
+* Broken/duplicate items were filtered out.
+* Each sample is stored as `{video_id}/video.mp4` + `{video_id}/metadata.json`.
+
+### Annotations (if applicable)
+
+If your dataset includes labels:
+
+* **Classes:** Safe / Not Safe
+* **Method:** Manual labeling using project guidelines
+
+(If you do not publish labels, you can remove this part.)
+
+## Personal and Sensitive Information
+
+Since the source is social media videos, the dataset may contain personal or sensitive content present in the original videos.
+No extra personal data was added by the dataset curators.
+Use the dataset only for non-commercial research and follow ethical data handling practices.
+
+## Bias, Risks, and Limitations
+
+* The dataset may reflect TikTok platform bias (recommendation, trends, sampling).
+* Harmful content definitions may be subjective and context-dependent.
+* The dataset may not represent all demographics or topics equally.
+
+### Recommendations
+
+* Use this dataset for research/education only.
+* Do not use it as the sole basis for real-world moderation decisions.
+* Report limitations and potential bias when publishing results.
+
+## Dataset Card Contact
+
+Please open an issue in the dataset repository for questions or problems.