Abstract
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.
Cite
CITATION STYLE
Pan, J. Z., Pavlova, S., Li, C., Li, N., Li, Y., & Liu, J. (2018). Content based fake news detection using knowledge graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11136 LNCS, pp. 669–683). Springer Verlag. https://doi.org/10.1007/978-3-030-00671-6_39
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