SocialED: A Python Library for Social Event Detection

TL;DR

SocialED is an open-source Python library that consolidates multiple social event detection algorithms and datasets, providing a modular, scalable, and well-documented tool for researchers and practitioners in social media analysis.

Contribution

It introduces a unified API and integrates 19 algorithms with 14 datasets, facilitating easier development and comparison in social event detection tasks.

Findings

Supports diverse preprocessing techniques

Ensures high efficiency on CPU and GPU

Maintains high code quality with testing and CI

Abstract

SocialED is a comprehensive, open-source Python library designed to support social event detection (SED) tasks, integrating 19 detection algorithms and 14 diverse datasets. It provides a unified API with detailed documentation, offering researchers and practitioners a complete solution for event detection in social media. The library is designed with modularity in mind, allowing users to easily adapt and extend components for various use cases. SocialED supports a wide range of preprocessing techniques, such as graph construction and tokenization, and includes standardized interfaces for training models and making predictions. By integrating popular deep learning frameworks, SocialED ensures high efficiency and scalability across both CPU and GPU environments. The library is built adhering to high code quality standards, including unit testing, continuous integration, and code coverage, ensuring that SocialED delivers robust, maintainable software. SocialED is publicly available at \url{https://github.com/RingBDStack/SocialED} and can be installed via PyPI.