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Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles

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Authors

Pilehvar, Mohammad 

Abstract

In this paper, we propose to adapt the four-staged pipeline proposed by Zubiaga et al. (2018) for the Rumor Verification task to the problem of Fake News Detection. We show that the recently released FNC-1 corpus covers two of its steps, namely the Tracking and the Stance Detection task. We identify asymmetry in length in the input to be a key characteristic of the latter step, when adapted to the framework of Fake News Detection, and propose to handle it as a specific type of CrossLevel Stance Detection. Inspired by theories from the field of Journalism Studies, we implement and test two architectures to successfully model the internal structure of an article and its interactions with a claim.

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Proceedings of the First Workshop on Fact Extraction and Verification (FEVER) at EMNLP 2018

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Sponsorship
Engineering and Physical Sciences Research Council (EP/M005089/1)
NERC (via Cranfield University) (NE/M009009/1)
The first author (CC) would like to thank the Siemens Machine Intelligence Group (CT RDA BAM MIC-DE, Munich) and the NERC DREAM CDT (grant no. 1945246) for partially funding this work. The third author (NC) is grateful for support from the UK EPSRC (grant no. EP/MOO5089/1)