Introduction
FaKnow (Fake Know), a unified Fake News Detection algorithms library based on PyTorch, is designed for reproducing and developing fake news detection algorithms. It includes 22 models(see at Integrated Models), covering 2 categories:
content based
social context
Integrated Models
Features
Unified Framework: provide a unified interface to cover a series of algorithm development processes, including data processing, model developing, training and evaluation
Generic Data Structure: use json as the file format read into the framework to fit the format of the data crawled down, allowing the user to customize the processing of different fields
Diverse Models: contains a number of representative fake news detection algorithms published in conferences or journals during recent years, including a variety of content-based and social context-based models
Convenient Usability: pytorch based style makes it easy to use with rich auxiliary functions like loss visualization, logging, parameter saving
Great Scalability: users just focus on the exposed API and inherit built-in classes to reuse most of the functionality and only need to write a little code to meet new requirements
Citation
@misc{faknow,
title = {{{FaKnow}}: {{A Unified Library}} for {{Fake News Detection}}},
shorttitle = {{{FaKnow}}},
author = {Zhu, Yiyuan and Li, Yongjun and Wang, Jialiang and Gao, Ming and Wei, Jiali},
year = {2024},
month = jan,
number = {arXiv:2401.16441},
eprint = {2401.16441},
primaryclass = {cs},
publisher = {{arXiv}},
archiveprefix = {arxiv},
keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning}
}
License
FaKnow has a MIT-style license, as found in the LICENSE file.