Reliability Estimation of News Media Sources: Birds of a Feather Flock Together

Reliability Estimation of News Media Sources: Birds of a Feather Flock Together

Stať ve sborníku v databázi WoS či Scopus

Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information. Recent research has shown that predicting sources’ reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. In this paper, we introduce a novel approach for source reliability estimation that leverages reinforcement learning strategies for estimating the reliability degree of news sources. Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web. We validated the effectiveness of our method on a news media reliability dataset that is an order of magnitude larger than comparable existing datasets. Results show that the estimated reliability degrees strongly correlates with journalists-provided scores (Spearman=0.80) and can effectively predict reliability labels (macro-avg. F1 score=81.05). We release our implementation and dataset, aiming to provide a valuable resource for the NLP community working on information verification.

Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information. Recent research has shown that predicting sources’ reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. In this paper, we introduce a novel approach for source reliability estimation that leverages reinforcement learning strategies for estimating the reliability degree of news sources. Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web. We validated the effectiveness of our method on a news media reliability dataset that is an order of magnitude larger than comparable existing datasets. Results show that the estimated reliability degrees strongly correlates with journalists-provided scores (Spearman=0.80) and can effectively predict reliability labels (macro-avg. F1 score=81.05). We release our implementation and dataset, aiming to provide a valuable resource for the NLP community working on information verification.

fact checking, factual reporting, information verification, news media, reinforcement learning

fact checking, factual reporting, information verification, news media, reinforcement learning

BURDISSO, S.; SÁNCHEZ-CORTÉS, D.; VILLATORO-TELLO, E.; MOTLÍČEK, P.

2026

16.06.2024

Association for Computational Linguistics (ACL)

Mexico City, Mexico

979-8-89176-114-8

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Naacl 2024

6893

6911

19

https://aclanthology.org/2024.naacl-long.383.pdf

burdisso_NAACL_2024