Classification of unlabeled online media

Abstract This work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user...

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Autores principales:	Sakthi Kumar Arul Prakash, Conrad Tucker
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/77dccb97c90d4dd4af3c9ddb0ef89e84
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spelling	oai:doaj.org-article:77dccb97c90d4dd4af3c9ddb0ef89e842021-12-02T17:04:05ZClassification of unlabeled online media10.1038/s41598-021-85608-52045-2322https://doaj.org/article/77dccb97c90d4dd4af3c9ddb0ef89e842021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85608-5https://doaj.org/toc/2045-2322Abstract This work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.Sakthi Kumar Arul PrakashConrad TuckerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sakthi Kumar Arul Prakash Conrad Tucker Classification of unlabeled online media
description	Abstract This work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.
format	article
author	Sakthi Kumar Arul Prakash Conrad Tucker
author_facet	Sakthi Kumar Arul Prakash Conrad Tucker
author_sort	Sakthi Kumar Arul Prakash
title	Classification of unlabeled online media
title_short	Classification of unlabeled online media
title_full	Classification of unlabeled online media
title_fullStr	Classification of unlabeled online media
title_full_unstemmed	Classification of unlabeled online media
title_sort	classification of unlabeled online media
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/77dccb97c90d4dd4af3c9ddb0ef89e84
work_keys_str_mv	AT sakthikumararulprakash classificationofunlabeledonlinemedia AT conradtucker classificationofunlabeledonlinemedia
_version_	1718381855776964608

Classification of unlabeled online media

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