TY - GEN
T1 - Fake News Detection Using Knowledge Vector
AU - He, Hansen
AU - Sun, Guozi
AU - Yu, Qiumei
AU - Li, Huakang
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - In recent years, social media takes the advantages of fast spreading speed, wide range and low cost to become the main channel for people to obtain news, which also makes it to be a hotbed for the proliferation of fake news, exposing users and society to huge risks. Due to the fact that there is some true information in fake news, traditional text feature detection algorithms are more difficult to detect the fake news. Therefore, it is necessary to use knowledge as auxiliary information to help detection. We propose a fake news detection framework using knowledge vectors, which can adopt existing and reliable news as knowledge sources and reduce the dependence on expert verification. The framework consists of three parts: event triple extraction based on reliable content, fusion knowledge vector and fake news detector. The experimental results on the data set show that the framework can fuse part of the knowledge information and optimize the detection performance.
AB - In recent years, social media takes the advantages of fast spreading speed, wide range and low cost to become the main channel for people to obtain news, which also makes it to be a hotbed for the proliferation of fake news, exposing users and society to huge risks. Due to the fact that there is some true information in fake news, traditional text feature detection algorithms are more difficult to detect the fake news. Therefore, it is necessary to use knowledge as auxiliary information to help detection. We propose a fake news detection framework using knowledge vectors, which can adopt existing and reliable news as knowledge sources and reduce the dependence on expert verification. The framework consists of three parts: event triple extraction based on reliable content, fusion knowledge vector and fake news detector. The experimental results on the data set show that the framework can fuse part of the knowledge information and optimize the detection performance.
KW - Fake news detection
KW - Knowledge representation
KW - Vector fusion
UR - http://www.scopus.com/inward/record.url?scp=85104484725&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-1160-5_15
DO - 10.1007/978-981-16-1160-5_15
M3 - Conference Proceeding
AN - SCOPUS:85104484725
SN - 9789811611599
T3 - Communications in Computer and Information Science
SP - 177
EP - 189
BT - Intelligent Computing and Block Chain - 1st BenchCouncil International Federated Conferences, FICC 2020, Revised Selected Papers
A2 - Gao, Wanling
A2 - Hwang, Kai
A2 - Wang , Changyun
A2 - Li, Weiping
A2 - Qiu, Zhigang
A2 - Wang, Lei
A2 - Zhou, Aoying
A2 - Qian, Weining
A2 - Jin, Cheqing
A2 - Zhang, Zhifei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020
Y2 - 30 October 2020 through 3 November 2020
ER -