---
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.