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A Deep Content-Based Model for Persian Rumor Verification

Authors:

Zoleikha Jahanbakhsh-Nagadeh,

Mohammad-Reza Feizi-Derakhshi,

Arash SharifiAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 1

Article No.: 12, Pages 1 - 29

https://doi.org/10.1145/3487289

Published: 29 November 2021 Publication History

Abstract

During the development of social media, there has been a transformation in social communication. Despite their positive applications in social interactions and news spread, it also provides an ideal platform for spreading rumors. Rumors can endanger the security of society in normal or critical situations. Therefore, it is important to detect and verify the rumors in the early stage of their spreading. Many research works have focused on social attributes in the social network to solve the problem of rumor detection and verification, while less attention has been paid to content features. The social and structural features of rumors develop over time and are not available in the early stage of rumor. Therefore, this study presented a content-based model to verify the Persian rumors on Twitter and Telegram early. The proposed model demonstrates the important role of content in spreading rumors and generates a better-integrated representation for each source rumor document by fusing its semantic, pragmatic, and syntactic information. First, contextual word embeddings of the source rumor are generated by a hybrid model based on ParsBERT and parallel CapsNets. Then, pragmatic and syntactic features of the rumor are extracted and concatenated with embeddings to capture the rich information for rumor verification. Experimental results on real-world datasets demonstrated that the proposed model significantly outperforms the state-of-the-art models in the early rumor verification task. Also, it can enhance the performance of the classifier from 2% to 11% on Twitter and from 5% to 23% on Telegram. These results validate the model's effectiveness when limited content information is available.

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Hashim Jawad Almarashy AFeizi-Derakhshi MSalehpour P(2024)Elevating Fake News Detection Through Deep Neural Networks, Encoding Fused Multi-Modal FeaturesIEEE Access10.1109/ACCESS.2024.341192612(82146-82155)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3411926
Farhadi ZFeizi-Derakhshi MBevrani HKim WIjaz M(2024)ERDeR: The Combination of Statistical Shrinkage Methods and Ensemble Approaches to Improve the Performance of Deep RegressionIEEE Access10.1109/ACCESS.2024.336806712(33361-33383)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368067
Rahimi MKiani V(2024)Improving stance detection accuracy in low-resource languages: a deep learning framework with ParsBERTInternational Journal of Data Science and Analytics10.1007/s41060-024-00630-wOnline publication date: 10-Sep-2024
https://doi.org/10.1007/s41060-024-00630-w
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Index Terms

A Deep Content-Based Model for Persian Rumor Verification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 21, Issue 1

January 2022

442 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3494068

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 29 November 2021

Accepted: 01 September 2021

Revised: 01 July 2021

Received: 01 October 2020

Published in TALLIP Volume 21, Issue 1

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Cited By

Hashim Jawad Almarashy AFeizi-Derakhshi MSalehpour P(2024)Elevating Fake News Detection Through Deep Neural Networks, Encoding Fused Multi-Modal FeaturesIEEE Access10.1109/ACCESS.2024.341192612(82146-82155)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3411926
Farhadi ZFeizi-Derakhshi MBevrani HKim WIjaz M(2024)ERDeR: The Combination of Statistical Shrinkage Methods and Ensemble Approaches to Improve the Performance of Deep RegressionIEEE Access10.1109/ACCESS.2024.336806712(33361-33383)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368067
Rahimi MKiani V(2024)Improving stance detection accuracy in low-resource languages: a deep learning framework with ParsBERTInternational Journal of Data Science and Analytics10.1007/s41060-024-00630-wOnline publication date: 10-Sep-2024
https://doi.org/10.1007/s41060-024-00630-w
Cheung TLam K(2023)Causal diffused graph-transformer network with stacked early classification loss for efficient stream classification of rumoursKnowledge-Based Systems10.1016/j.knosys.2023.110807277:COnline publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110807
BOZUYLA M(2022)ADVANCED TURKISH FAKE NEWS PREDICTION WITH BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERSÇift Yönlü Transformatör Kodlayıcı Temsilleriyle Gelişmiş Türkçe Sahte Haber TahminiKonya Journal of Engineering Sciences10.36306/konjes.99506010:3(750-761)Online publication date: 1-Sep-2022
https://doi.org/10.36306/konjes.995060

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