Content based fake news detection using knowledge graphs

Jeff Z. Pan*, Siyana Pavlova, Chenxi Li, Ningxi Li, Yangmei Li, Jinshuo Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

101 Citations (Scopus)


This paper addresses the problem of fake news detection. There are many works already in this space; however, most of them are for social media and not using news content for the decision making. In this paper, we propose some novel approaches, including the B-TransE model, to detecting fake news based on news content using knowledge graphs. In our solutions, we need to address a few technical challenges. Firstly, computational-oriented fact checking is not comprehensive enough to cover all the relations needed for fake news detection. Secondly, it is challenging to validate the correctness of the extracted triples from news articles. Our approaches are evaluated with the Kaggle’s ‘Getting Real about Fake News’ dataset and some true articles from main stream media. The evaluations show that some of our approaches have over 0.80 F1-scores.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings
EditorsD Vrandečić
PublisherSpringer Verlag
Number of pages15
ISBN (Electronic)9783030006716
ISBN (Print)9783030006709
Publication statusPublished - 18 Dec 2018
Event17th International Semantic Web Conference, ISWC 2018 - Monterey, United States
Duration: 8 Oct 201812 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11136 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Semantic Web Conference, ISWC 2018
Country/TerritoryUnited States


Dive into the research topics of 'Content based fake news detection using knowledge graphs'. Together they form a unique fingerprint.

Cite this