SocialED: A Python Library for Social Event Detection

TL;DR

SocialED addresses the need for a unified, extensible toolkit for social event detection by integrating 19 detection algorithms and 14 datasets into a single Python library. It offers a unified API with preprocessing, training, and prediction steps and a modular design that supports easy extension and customization. The library emphasizes robustness through CI, high test coverage, and comprehensive documentation, and is designed to run efficiently on CPU and GPU with popular frameworks like PyTorch and Transformers. By lowering the barrier to developing and evaluating SED methods, SocialED facilitates rapid experimentation and practical deployment across research and industry contexts.

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.

Figures (1)

• Figure 1: Demonstration of SocialED's unified API design.