Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8599
Title: Re-Think Before You Share: A Comprehensive Study on Prioritizing Check-Worthy Claims
Authors: Kartal, Yavuz Selim
Kutlu, Mücahid
Keywords: Task analysis
Fake news
Data models
Training
Bit error rate
Training data
Predictive models
Check-worthy claims
fact-checking
misinformation
Issue Date: 2022
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Source: Kartal, Y. S., & Kutlu, M. (2022). Re-Think Before You Share: A Comprehensive Study on Prioritizing Check-Worthy Claims. IEEE Transactions on Computational Social Systems.
Abstract: The massive amount of misinformation spreading on the internet on a daily basis has enormous negative impacts on societies. Therefore, we need systems to help fact-checkers to combat misinformation and to raise public awareness of this important problem. In this article, we propose a hybrid model which combines bidirectional encoder representations from transformer (BERT) model with various features to prioritize claims based on their check-worthiness. Features we use include domain-specific controversial topics (CT), word embeddings (WE), part-of-speech (POS) tags, and others. In addition, we explore various ways of increasing labeled data size to effectively train the models, such as increasing positive (IncPos) samples, active learning (AL), and utilizing labeled data in other languages. In our extensive experiments, we show that our model outperforms all state-of-the-art models in test collections of Conference and Labs of Evaluation Forum (CLEF) CheckThat! Lab (CTL) 2018 and 2019. In addition, when positive samples are increased in the training set, our model achieves the best mean average precision (MAP) score reported so far for the test collection of CTL 2020. Furthermore, we show that cross-lingual training is effective for prioritizing Arabic and Turkish claims, but not for English.
URI: https://doi.org/10.1109/TCSS.2021.3138642
https://hdl.handle.net/20.500.11851/8599
ISSN: 2329-924X
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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