Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning

Chen, Xueqin; Zhang, Fan; Zhang, Fengli; Bonsangue, Marcello M.

doi:10.1016/j.ipm.2021.102678

“…The above methods are only part of the representative work of rumor detection. Since deep learning has made incredible achievements in rumor detection, many researchers have extracted visual feature ([ 13 , 42 ]), propagation feature ([ 65 , 67 ]) and user social background feature ([ 16 ]) of rumors from rich data. Based on the fusion of multiple features, more novel and excellent methods are proposed, such as multi-task methods [ 56 ], adversarial learning [ 101 ], semi-supervised methods [ 30 ], weakly supervised methods [ 37 , 109 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

Research status of deep learning methods for rumor detection

Tan

¹

,

Wang

²

,

Jia

³

et al. 2022

Multimed Tools Appl

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To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

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“…For example, 65% of users prefer the internet to search health-related topics [2,3,4]. According to the survey in 2017, by Pew Research Center, 88% of American people have quick access to the internet at home and 81% of them get updates of news from the internet [5]. Therefore, it can be determined that the users make maximum usage of the internet for information access.…”

Section: Introductionmentioning

confidence: 99%

“…For example, during the recent Covid-19 pandemic, misinformation about ingesting fish tank cleaning products can cure the virus or 5G networks generate radiations that triggers the virus or statement like "coronavirus is just like the flu" or "coronavirus is an engineered bioweapon" had an impact on people that they started believing the misinformation. Such misinformation causes panic amongst citizens and may lead to death [5,10]. During 2014, Ebola outbreak, misinformation on the web and social media about some products which can cure Ebola had led to deaths [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting and Fact-checking Misinformation using “Veracity Scanning Model”

Barve¹,

Saini²,

Kotecha³

et al. 2022

IJACSA

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The expeditious flow of information over the web and its ease of convenience has increased the fear of the rampant spread of misinformation. This poses a health threat and an unprecedented issue to the world impacting people's life. To cater to this problem, there is a need to detect misinformation. Recent techniques in this area focus on static models based on feature extraction and classification. However, data may change at different time intervals and the veracity of data needs to be checked as it gets updated. There is a lack of models in the literature that can handle incremental data, check the veracity of data and detect misinformation. To fill this gap, authors have proposed a novel Veracity Scanning Model (VSM) to detect misinformation in the healthcare domain by iteratively factchecking the contents evolving over the period of time. In this approach, the healthcare web URLs are classified as legitimate or non-legitimate using sentiment analysis as a feature, document similarity measures to perform fact-checking of URLs, and incremental learning to handle the arrival of incremental data. The experimental results show that the Jaccard Distance measure has outperformed other techniques with an accuracy of 79.2% with Random Forest classifier while the Cosine similarity measure showed less accuracy of 60.4% with the Support Vector Machine classifier. Also, when implemented as an algorithm Euclidean distance showed an accuracy of 97.14% and 98.33% respectively for train and test data.

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“…Since deep learning has made incredible achievements in rumor detection, many researchers have extracted visual feature ( J. ; Jin et al (2017)), propagation feature Wong (2017, 2018b)) and user social background feature ( X. Chen, Zhou, Zhang, and Bonsangue (2021)) of rumors from rich data. Based on the fusion of multiple features, more novel and excellent methods are proposed, such as multi-task methods (Q.…”

Section: Introductionmentioning

confidence: 99%

Research Status of Deep Learning Methods for Rumor Detection

Tan,

Wang,

Jia

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

show abstract

Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning

Cited by 29 publications

References 45 publications

Research status of deep learning methods for rumor detection

Research status of deep learning methods for rumor detection

Detecting and Fact-checking Misinformation using “Veracity Scanning Model”

Research Status of Deep Learning Methods for Rumor Detection

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