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A novel rumor detection with multi-objective loss functions in online social networks

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor...

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Detalles Bibliográficos
Autores principales: Wan, Pengfei, Wang, Xiaoming, Pang, Guangyao, Wang, Liang, Min, Geyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650513/
https://www.ncbi.nlm.nih.gov/pubmed/36407849
http://dx.doi.org/10.1016/j.eswa.2022.119239
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author Wan, Pengfei
Wang, Xiaoming
Pang, Guangyao
Wang, Liang
Min, Geyong
author_facet Wan, Pengfei
Wang, Xiaoming
Pang, Guangyao
Wang, Liang
Min, Geyong
author_sort Wan, Pengfei
collection PubMed
description COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.
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spelling pubmed-96505132022-11-14 A novel rumor detection with multi-objective loss functions in online social networks Wan, Pengfei Wang, Xiaoming Pang, Guangyao Wang, Liang Min, Geyong Expert Syst Appl Article COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%. Elsevier Ltd. 2023-03-01 2022-11-11 /pmc/articles/PMC9650513/ /pubmed/36407849 http://dx.doi.org/10.1016/j.eswa.2022.119239 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wan, Pengfei
Wang, Xiaoming
Pang, Guangyao
Wang, Liang
Min, Geyong
A novel rumor detection with multi-objective loss functions in online social networks
title A novel rumor detection with multi-objective loss functions in online social networks
title_full A novel rumor detection with multi-objective loss functions in online social networks
title_fullStr A novel rumor detection with multi-objective loss functions in online social networks
title_full_unstemmed A novel rumor detection with multi-objective loss functions in online social networks
title_short A novel rumor detection with multi-objective loss functions in online social networks
title_sort novel rumor detection with multi-objective loss functions in online social networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650513/
https://www.ncbi.nlm.nih.gov/pubmed/36407849
http://dx.doi.org/10.1016/j.eswa.2022.119239
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