Content Based Fake News Detection Using Knowledge Graphs

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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
Subtitle of host publication17th International Semantic Web Conference, Monterey, CA, USA, October 8–12, 2018, Proceedings, Part I
EditorsDenny Vrandeci, Kalina Bontcheva, Mari Carmen Suárez-Figueroa, Valentina Presutti, Irene Celino, Marta Sabou, Lucie-Aimée Kaffee, Elena Simperl
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages15
ISBN (Electronic)978-3-030-00671-6
ISBN (Print)978-3-030-00670-9
Publication statusPublished - 18 Sep 2018
EventThe 17th International Semantic Web Conference 2018 - Monterey, United States
Duration: 8 Oct 201812 Oct 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 17th International Semantic Web Conference 2018
Abbreviated titleISWC 2018
CountryUnited States
Internet address

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