TY - GEN
T1 - Incorporating Relational Knowledge in Explainable Fake News Detection
AU - Wu, Kun
AU - Yuan, Xu
AU - Ning, Yue
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The greater public has become aware of the rising prevalence of untrustworthy information in online media. Extensive adaptive detection methods have been proposed for mitigating the adverse effect of fake news. Computational methods for detecting fake news based on the news content have several limitations, such as: 1) Encoding semantics from original texts is limited to the structure of the language in the text, making both bag-of-words and embedding-based features deceptive in the representation of a fake news, and 2) Explainable methods often neglect relational contexts in fake news detection. In this paper, we design a knowledge graph enhanced framework for effectively detecting fake news while providing relational explanation. We first build a credential-based multi-relation knowledge graph by extracting entity relation tuples from our training data and then apply a compositional graph convolutional network to learn the node and relation embeddings accordingly. The pre-trained graph embeddings are then incorporated into a graph convolutional network for fake news detection. Through extensive experiments on three real-world datasets, we demonstrate the proposed knowledge graph enhanced framework has significant improvement in terms of fake news detection as well as structured explainability.
AB - The greater public has become aware of the rising prevalence of untrustworthy information in online media. Extensive adaptive detection methods have been proposed for mitigating the adverse effect of fake news. Computational methods for detecting fake news based on the news content have several limitations, such as: 1) Encoding semantics from original texts is limited to the structure of the language in the text, making both bag-of-words and embedding-based features deceptive in the representation of a fake news, and 2) Explainable methods often neglect relational contexts in fake news detection. In this paper, we design a knowledge graph enhanced framework for effectively detecting fake news while providing relational explanation. We first build a credential-based multi-relation knowledge graph by extracting entity relation tuples from our training data and then apply a compositional graph convolutional network to learn the node and relation embeddings accordingly. The pre-trained graph embeddings are then incorporated into a graph convolutional network for fake news detection. Through extensive experiments on three real-world datasets, we demonstrate the proposed knowledge graph enhanced framework has significant improvement in terms of fake news detection as well as structured explainability.
KW - Explainable machine learning
KW - Fake news detection
KW - Knowledge graphs
UR - http://www.scopus.com/inward/record.url?scp=85111039771&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111039771&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75768-7_32
DO - 10.1007/978-3-030-75768-7_32
M3 - Conference contribution
AN - SCOPUS:85111039771
SN - 9783030757670
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 403
EP - 415
BT - Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings
A2 - Karlapalem, Kamal
A2 - Cheng, Hong
A2 - Ramakrishnan, Naren
A2 - Agrawal, R. K.
A2 - Reddy, P. Krishna
A2 - Srivastava, Jaideep
A2 - Chakraborty, Tanmoy
T2 - 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
Y2 - 11 May 2021 through 14 May 2021
ER -