Published on Sat Apr 21 2018

Integrating Stance Detection and Fact Checking in a Unified Corpus

Ramy Baly, Mitra Mohtarami, James Glass, Lluis Marquez, Alessandro Moschitti, Preslav Nakov

A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources. This setup is not directly supported by existing datasets, which treat document retrieval, source credibility, stance detection andrationale extraction as independent tasks. In this paper, we support the intertwining of these tasks as

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Abstract

A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim's factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. In this paper, we support the interdependencies between these tasks as annotations in the same corpus. We implement this setup on an Arabic fact checking corpus, the first of its kind.

Thu May 21 2020
NLP
Stance Prediction and Claim Verification: An Arabic Perspective
This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations are insufficient for verifying claims without context or evidence.
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Wed Apr 28 2021
NLP
AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
AraStance covers false and true claims from multiple domains. It is well-balanced between related and unrelated documents with respect to the claims. Our best model achieves an accuracy of 85%.
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Fri Apr 20 2018
NLP
ClaimRank: Detecting Check-Worthy Claims in Arabic and English
ClaimRank is an online system for detecting check-worthy claims. It is trained on annotations from nine reputable fact-checking organizations. ClaimRank supports both Arabic and English.
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Tue Oct 29 2019
NLP
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. Machine learning methods require a large corpus with reliable annotations for the different tasks in the fact- checking process. We present a new substantially sized mixed-domain corpus with annotations of good quality.
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Fri Jun 07 2019
Machine Learning
FAKTA: An Automatic End-to-End Fact Checking System
FAKTA is a unified framework that integrates various components of a fact checking process. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level.
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Thu Aug 26 2021
NLP
A Survey on Automated Fact-Checking
Fact-checking has become increasingly important due to the speed with which misinformation can spread in the modern media ecosystem. Researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, and databases.
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