pyMADOC

Python package to download and combine parts of MADOC dataset from Zenodo. The MADOC dataset contains social media posts from multiple platforms (Reddit, Voat, Bluesky, and Koo), making it easy to study cross-platform content and community dynamics.

Features

• Easy download of platform-specific data files
• Automatic pairing of Reddit-Voat community data
• Both Python API and Command Line Interface
• Support for direct DataFrame loading
• Efficient parquet file format

Installation

bash pip install pymadoc

Usage

As a Python Package

```python from pymadoc import listavailabledata, downloadfile, downloadcommunity_pair

List available platforms and communities

datainfo = listavailabledata() print(datainfo["platforms"]) # ['reddit', 'voat', 'bluesky', 'koo'] print(data_info["communities"]) # ['CringeAnarchy', 'fatpeoplehate', ...]

Download a specific file

For Reddit/Voat, specify both platform and community

filepath = downloadfile("reddit", community="funny", output_dir="data")

For Bluesky/Koo, specify only platform

filepath = downloadfile("bluesky", output_dir="data")

Load directly as DataFrame

df = downloadfile("reddit", community="funny", asdataframeTrue)

Download and combine Reddit-Voat community pair

As files

redditfile, voatfile = downloadcommunitypair("funny", output_dir="data")

As combined DataFrame

combineddf = downloadcommunitypair("funny", asdataframe=True) ```

Command Line Interface

List available platforms and communities: bash pymadoc list

Download a specific file: ```bash

Reddit/Voat (requires community)

pymadoc download reddit --community funny --output-dir data

Bluesky/Koo

pymadoc download bluesky --output-dir data ```

Download Reddit-Voat community pair: bash pymadoc pair funny --output-dir data

Available Data

Platforms

• Reddit: Community-specific posts and comments
• Voat: Community-specific posts and comments
• Bluesky: Platform-wide posts
• Koo: Platform-wide posts

Communities (Reddit/Voat only)

• CringeAnarchy
• fatpeoplehate
• funny
• gaming
• gifs
• greatawakening
• KotakuInAction
• MensRights
• milliondollarextreme
• pics
• technology
• videos

Data Format

All files are stored in parquet format for efficient storage and fast loading. Each file contains the following columns: - Platform-specific post/comment IDs - Content text - Timestamps - User information - Engagement metrics

Requirements

• Python 3.6 or higher
• pandas
• requests
• tqdm

Citation

If you use this package or the MADOC dataset in your research, please cite:

Mitrovic Dankulov, M., Tomašević, A., Maletic, S., Andjelkovic, M., Vranic, A., Cvetkovic, D., Stupovski, B., Vudragovic, D., Major, S., & Bogojević, A. (2025). MADOC: Multi-Platform Aggregated Dataset of Online Communities (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14637314

Or BibTeX:

``` @MISC{mitrovicDankulov2025madoc, title = "MADOC: Multi-Platform Aggregated Dataset of Online Communities", author = "Mitrovic Dankulov, Marija and Tomašević, Aleksandar and Maletic, Slobodan and Andjelkovic, Miroslav and Vranic, Ana and Cvetkovic, Darja and Stupovski, Boris and Vudragovic, Dusan and Major, Sara and Bogojević, Aleksandar", publisher = "Zenodo", abstract = "The Multi-platform Aggregated Dataset of Online Communities (MADOC) is a comprehensive dataset that facilitates computational social science research by providing a unified, standardized dataset for cross-platform analysis of online social dynamics. MADOC aggregates and standardizes data from four distinct platforms: Bluesky, Koo, Reddit, and Voat, spanning from 2012 to 2024. The dataset includes 18.9 million posts, 236 million comments, and data from 23.1 million unique users across all platforms, with a particular focus on understanding community dynamics, user migration patterns, and the evolution of toxic behavior across platforms. By providing standardized data structures and FAIR-compliant access through Zenodo, MADOC enables researchers to conduct comparative analyses of user behavior, interaction networks, and content sentiment across diverse social media environments. The dataset's unique value lies in its cross-platform scope, standardized structure, and rich metadata, making it particularly suitable for studying societal phenomena such as community formation, toxic behavior propagation, and user migration patterns in response to platform moderation policies.", year = 2025, url = "https://zenodo.org/records/14637314", keywords = "Social Media; Online Social Networking; Social Network Analysis", doi = "10.5281/ZENODO.14637314" }

```

License

MIT License