FaKnow: A Unified Library for Fake News Detection
Yiyuan Zhu Yongjun Li Jialiang Wang Ming Gao Jiali Wei
Over the past years, a large number of fake news detection algorithms based
on deep learning have emerged. However, they are often developed under
different frameworks, each mandating distinct utilization methodologies,
consequently hindering reproducibility. Additionally, a substantial amount of
redundancy characterizes the code development of such fake news detection
models. To address these concerns, we propose FaKnow, a unified and
comprehensive fake news detection algorithm library. It encompasses a variety
of widely used fake news detection models, categorized as content-based and
social context-based approaches. This library covers the full spectrum of the
model training and evaluation process, effectively organizing the data, models,
and training procedures within a unified framework. Furthermore, it furnishes a
series of auxiliary functionalities and tools, including visualization, and
logging. Our work contributes to the standardization and unification of fake
news detection research, concurrently facilitating the endeavors of researchers
in this field. The open-source code and documentation can be accessed at
https://github.com/NPURG/FaKnow and https://faknow.readthedocs.io,
respectively.