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4CAT Capture & Analysis Toolkit

The 4CAT Capture and Analysis Toolkit provides modular data capture & analysis for a variety of social media platforms. Its goal is to make the capture and analysis of data from these platforms accessible to people through a web interface, without requiring any programming or web scraping skills.


Cite this software

What 4CAT Capture & Analysis Toolkit can do for you

4CAT: Capture and Analysis Toolkit

DOI: 10.5117/CCR2022.2.007.HAGE DOI: 10.5281/zenodo.4742622 License: MPL 2.0 Requires Python 3.8 Docker image status

4CAT is a research tool that can be used to analyse and process data from online social platforms. Its goal is to make the capture and analysis of data from these platforms accessible to people through a web interface, without requiring any programming or web scraping skills. Our target audience is researchers, students and journalists interested using Digital Methods in their work.

In 4CAT, you create a dataset from a given platform according to a given set of parameters; the result of this (usually a CSV or JSON file containing matching items) can then be downloaded or analysed further with a suite of analytical 'processors', which range from simple frequency charts to more advanced analyses such as the generation and visualisation of word embedding models.

4CAT has a (growing) number of supported data sources corresponding to popular platforms that are part of the tool, but you can also add additional data sources using 4CAT's Python API. The following data sources are currently supported actively and can be used to collect data with 4CAT directly:

  • 4chan and 8kun
  • Telegram
  • Tumblr

The following platforms are supported through Zeeschuimer, with which you can collect data to import into 4CAT for analysis:

  • Instagram (posts)
  • TikTok (posts and comments)
  • 9gag
  • Imgur
  • LinkedIn
  • Gab
  • Douyin
  • X/Twitter

It is also possible to upload data collected with other tools as CSV files. The following tools are explicitly supported but other data can also be uploaded as long as it is formatted as CSV:

A number of other platforms have built-in support that is untested, or requires e.g. special API access. You can view the data sources in our wiki or review the data sources' code in the GitHub repository.


You can install 4CAT locally or on a server via Docker or manually. For easiest installation, we recommend copying our docker-compose.yml file, .env file, and running this terminal command in the folder where those files have been saved:

docker-compose up -d

In depth instructions on both Docker installation and manual installation can be found in our wiki. A video walkthrough installing 4CAT via Docker can be found on YouTube here.

Currently scraping of 4chan, 8chan, and 8kun require additional steps; please see the wiki.

Please check our issues and create one if you experience any problems (pull requests are also very welcome).

Upgrading 4CAT

Instructions on upgrading 4CAT from previous versions can be found in our wiki.


4CAT is a modular tool and easy to extend. The following two folders in the repository are of interest for this:

  • datasources: Data source definitions. This is a set of configuration options, database definitions and python scripts to process this data with. If you want to set up your own data sources, refer to the wiki.
  • processors: A collection of data processing scripts that can plug into 4CAT to manipulate or process datasets created with 4CAT. There is an API you can use to make your own processors.

Credits & License

4CAT was created at OILab and the Digital Methods Initiative at the University of Amsterdam. The tool was inspired by DMI-TCAT, a tool with comparable functionality that can be used to scrape and analyse Twitter data.

4CAT development is supported by the Dutch PDI-SSH foundation through the CAT4SMR project.

4CAT is licensed under the Mozilla Public License, 2.0. Refer to the LICENSE file for more information.

Logo of 4CAT Capture & Analysis Toolkit
Programming languages
  • Python 79%
  • JavaScript 10%
  • HTML 7%
  • CSS 3%
  • Other 1%
</>Source code

Participating organisations

Digital Methods Initiative

Reference papers



Stijn Peeters

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Zeeschuimer is a browser extension that monitors traffic while browsing social media, and collects seen posts/items for later systematic analysis. Its target audience is researchers who study content on social media platforms that resist conventional scraping or API-based data collection.

Updated 1 week ago