Dataset card

README.md

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+---
+license: other
+license_name: research-use
+pretty_name: TikTok Videos, 4.5 Billion
+size_categories:
+  - n>1T
+task_categories:
+  - text-classification
+  - text-generation
+  - feature-extraction
+language:
+  - en
+  - es
+  - pt
+  - id
+  - ar
+tags:
+  - tiktok
+  - social-media
+  - short-video
+  - recommender-systems
+  - social-network-analysis
+configs:
+  - config_name: default
+    data_files: "videos-*.parquet"
+---
+
+# TikTok Videos: 4.5 billion posts with engagement metrics
+
+4.5 billion TikTok video records with captions, engagement counts, sound
+identifiers and timing. Collected from TikTok's mobile API over roughly three
+weeks. Every `content_id` appears exactly once.
+
+This is the largest public TikTok dataset I am aware of. It is released as-is,
+for research.
+
+## What is in it
+
+27 Parquet files, zstd compressed, about 289 GB in total. One row per video.
+
+| Column | Type | Description |
+|---|---|---|
+| `content_id` | uint64 | TikTok's video ID. Unique across the dataset |
+| `create_time` | datetime | When the video was posted |
+| `desc` | string | The caption, as written by the creator |
+| `mentions` | list[uint64] | Account IDs tagged in the video |
+| `duration` | uint16 | Length in seconds |
+| `is_video` | uint8 | 1 for video, 0 for a photo post |
+| `music_id` | uint64 | The sound used. Join key across videos |
+| `music_title` | string | Name of the sound |
+| `views` | uint64 | Play count at collection time |
+| `likes` | uint64 | |
+| `comments` | uint64 | Comment count |
+| `shares` | uint64 | |
+| `saves` | uint64 | Bookmarks. Often the earliest signal that something is moving |
+| `country` | string | Two-letter country code |
+| `language` | string | Language code |
+| `is_ad` | uint8 | Marked as sponsored |
+
+## Getting started
+
+```python
+import duckdb
+
+# Query it without loading it. No unpacking, no full download needed.
+duckdb.sql("""
+  SELECT music_id, music_title, count(*) AS videos, sum(views) AS plays
+  FROM 'videos-*.parquet'
+  WHERE create_time >= '2025-01-01'
+  GROUP BY 1, 2 ORDER BY plays DESC LIMIT 20
+""").show()
+```
+
+```python
+import pandas as pd
+df = pd.read_parquet("videos-00.parquet", columns=["content_id", "views", "desc"])
+```
+
+```python
+from datasets import load_dataset
+ds = load_dataset("kuben-developer/tiktok-videos-4b", streaming=True)
+```
+
+One file is about 10 GB and holds roughly 167 million videos, so start with a
+single file before pulling all 27.
+
+## How it was collected
+
+Through the private HTTP API that TikTok's Android app uses, rather than the web
+endpoints or a headless browser. Requests are signed the way the app signs them,
+from anonymous device registrations. There is no login anywhere in the pipeline,
+no account, and no session cookie, so nothing here is account-gated content.
+
+The method is written up in full at
+<https://tiktok-api.seeksocial.io>.
+
+## Things to know before you use it
+
+**The counts are a snapshot, not a time series.** Every engagement number is
+whatever it was at the moment that row was collected, somewhere in a three week
+window. A video collected on day one and a video collected on day twenty have had
+different amounts of time to accumulate views. Do not compare raw counts across
+distant `create_time` values without normalising for age.
+
+**Rows are grouped by creator, not shuffled.** The export preserves the storage
+order, which clusters each creator's videos together. If you are training on this,
+shuffle. Reading it sequentially gives you highly correlated batches.
+
+**Creator identity is not included.** There is no author ID, username or profile
+data. You can group videos by sound, hashtag mention or caption, but not by who
+posted them. This is deliberate.
+
+**Media URLs are not included.** TikTok's CDN links carry signed expiry
+parameters and stop working within days, so shipping 539 GB of them would have
+been 539 GB of dead links.
+
+**Coverage is a sample, not a census.** This is 27 of 32 storage partitions,
+split on a hash of the creator ID, so it is an unbiased random subset of what was
+collected rather than a filtered one. What was collected is itself not all of
+TikTok.
+
+**Deduplicated on `content_id`.** The source table had about 10% repeat rows from
+overlapping collection passes. Those are collapsed, keeping the most recently
+seen version of each video.
+
+**`country` and `language` are TikTok's labels**, inferred by them, not verified.
+They are wrong often enough that you should not treat them as ground truth.
+
+## Licence and responsible use
+
+Released for research and educational use.
+
+Captions are written by real people and this dataset is personal data under GDPR,
+the UK GDPR and CCPA regardless of the fact that it was publicly posted. If you
+are in a jurisdiction those apply to, that obligation is yours the moment you
+download it. Do not use this to identify, profile, target or contact individuals.
+
+Collection was contrary to TikTok's terms of service. This dataset is not
+affiliated with, endorsed by, or connected to TikTok or ByteDance.
+
+If you are named in this data and want your rows removed, open a discussion on
+this repository.