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.

Zhu Yiyuan

Li Yongjun

Wang Jialiang

Gao Ming

Wei Jiali

Data Intelligence

FaKnow: A Unified Library for Fake News Detection

FaKnow is proposed, a unified and comprehensive fake news detection algorithm library that encompasses a variety of widely used fake news detection models, categorized as content-based and social context-based approaches.

Category	Quartile
计算机科学	3
计算机科学, 计算机信息系统	3

FaKnow: A Unified Library for Fake News Detection

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	3