SocialED: A Python Library for Social Event Detection
This library addresses the problem of fragmented resources for social event detection, offering a complete solution for researchers and practitioners, though it is incremental as it consolidates existing methods.
The authors tackled the lack of a comprehensive tool for social event detection by developing SocialED, an open-source Python library that integrates 19 algorithms and 14 datasets, providing a unified API and modular design for researchers and practitioners.
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.