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Paper

DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

MPS-Authors
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Popat,  Kashyap
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Yates,  Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1809.06416.pdf
(Preprint), 2MB

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Citation

Popat, K., Mukherjee, S., Yates, A., & Weikum, G. (2018). DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. Retrieved from http://arxiv.org/abs/1809.06416.


Cite as: http://hdl.handle.net/21.11116/0000-0002-5EE1-7
Abstract
Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.