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A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media

BACKGROUND: Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people’s daily lives, national economy, social stability, et...

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Autores principales: Lu, Heng-yang, Fan, Chenyou, Song, Xiaoning, Fang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384041/
https://www.ncbi.nlm.nih.gov/pubmed/34497874
http://dx.doi.org/10.7717/peerj-cs.688
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author Lu, Heng-yang
Fan, Chenyou
Song, Xiaoning
Fang, Wei
author_facet Lu, Heng-yang
Fan, Chenyou
Song, Xiaoning
Fang, Wei
author_sort Lu, Heng-yang
collection PubMed
description BACKGROUND: Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people’s daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages. METHODS: This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods. RESULTS: Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model.
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spelling pubmed-83840412021-09-07 A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media Lu, Heng-yang Fan, Chenyou Song, Xiaoning Fang, Wei PeerJ Comput Sci Artificial Intelligence BACKGROUND: Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people’s daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages. METHODS: This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods. RESULTS: Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model. PeerJ Inc. 2021-08-20 /pmc/articles/PMC8384041/ /pubmed/34497874 http://dx.doi.org/10.7717/peerj-cs.688 Text en ©2021 Lu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Lu, Heng-yang
Fan, Chenyou
Song, Xiaoning
Fang, Wei
A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media
title A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media
title_full A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media
title_fullStr A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media
title_full_unstemmed A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media
title_short A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media
title_sort novel few-shot learning based multi-modality fusion model for covid-19 rumor detection from online social media
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384041/
https://www.ncbi.nlm.nih.gov/pubmed/34497874
http://dx.doi.org/10.7717/peerj-cs.688
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