Abstract

Ramy Baly, Mitra Mohtarami, James Glass, Lluís Màrquez, Alessandro Moschitti, Preslav Nakov. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.

Highlights

  • Fact checking has recently emerged as an important research topic due to the unprecedented amount of fake news and rumors that are flooding the Internet in order to manipulate people’s opinions (Darwish et al, 2017a; Mihaylov et al, 2015a,b; Mihaylov and Nakov, 2016) or to influence the outcome of major events such as political elections (Lazer et al, 2018; Vosoughi et al, 2018)

  • Despite the interdependency between fact checking and stance detection, research on these two problems has not been previously supported by an integrated corpus

  • We have described a novel corpus that unifies stance detection, stance rationale, relevant document retrieval, and fact checking

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Summary

Introduction

Fact checking has recently emerged as an important research topic due to the unprecedented amount of fake news and rumors that are flooding the Internet in order to manipulate people’s opinions (Darwish et al, 2017a; Mihaylov et al, 2015a,b; Mihaylov and Nakov, 2016) or to influence the outcome of major events such as political elections (Lazer et al, 2018; Vosoughi et al, 2018). Automatic fact checking typically involves retrieving potentially relevant documents (news articles, tweets, etc.), determining the stance of each document with respect to the claim, and predicting the claim’s factuality by aggregating the strength of the different stances, taking into consideration the reliability of the documents’ sources (news medium, Twitter account, etc.). Despite the interdependency between fact checking and stance detection, research on these two problems has not been previously supported by an integrated corpus. This is a gap we aim to bridge by retrieving documents for each claim and annotating them for stance, ensuring a natural distribution of the stance labels

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