Summary of Socialed: a Python Library For Social Event Detection, by Kun Zhang et al.
SocialED: A Python Library for Social Event Detection
by Kun Zhang, Xiaoyan Yu, Pu Li, Hao Peng, Philip S. Yu
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Digital Libraries (cs.DL); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The SocialED library is an open-source, comprehensive Python tool designed to support social event detection tasks. It integrates 19 algorithms and 14 datasets, providing a unified API with detailed documentation for researchers and practitioners. The library’s modular design allows users to easily adapt and extend components for various use cases. SocialED supports preprocessing techniques such as graph construction and tokenization, as well as standardized interfaces for training models and making predictions. By integrating popular deep learning frameworks, it ensures high efficiency and scalability across CPU and GPU environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SocialED is a special computer program that helps find important events in social media. It can do this by using 19 different methods to look at lots of data from the internet. The program is very useful because it makes it easy for researchers and experts to use and adapt its tools. SocialED also works well on many different types of computers, making it fast and efficient. |
Keywords
» Artificial intelligence » Deep learning » Event detection » Tokenization
