Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System

This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inherently explainable artificial intelligence technique, which utilizes machine learning to train an expert system, for this purpose. It considers the role...

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Autores principales:	Xinyu (Sherwin) Liang, Jeremy Straub
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intentionally deceptive online content fake news message characteristics machine learning trained expert system social media Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/a73b553bc4b14367b11ba7d897bf5470
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spelling	oai:doaj.org-article:a73b553bc4b14367b11ba7d897bf54702021-11-11T19:06:01ZDeceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System10.3390/s212170831424-8220https://doaj.org/article/a73b553bc4b14367b11ba7d897bf54702021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7083https://doaj.org/toc/1424-8220This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inherently explainable artificial intelligence technique, which utilizes machine learning to train an expert system, for this purpose. It considers the role of three factors (textual context, speaker background, and emotion) in fake news detection analysis and evaluates the efficacy of using key factors, but not the inherently subjective processing of post text itself, to identify deceptive online content. This paper presents initial work on a potential deceptive content detection tool and also, through the networks that it presents for this purpose, considers the interrelationships of factors that can be used to determine whether a post is deceptive content or not and their comparative importance.Xinyu (Sherwin) LiangJeremy StraubMDPI AGarticleintentionally deceptive online contentfake newsmessage characteristicsmachine learning trained expert systemsocial mediaChemical technologyTP1-1185ENSensors, Vol 21, Iss 7083, p 7083 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intentionally deceptive online content fake news message characteristics machine learning trained expert system social media Chemical technology TP1-1185
spellingShingle	intentionally deceptive online content fake news message characteristics machine learning trained expert system social media Chemical technology TP1-1185 Xinyu (Sherwin) Liang Jeremy Straub Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System
description	This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inherently explainable artificial intelligence technique, which utilizes machine learning to train an expert system, for this purpose. It considers the role of three factors (textual context, speaker background, and emotion) in fake news detection analysis and evaluates the efficacy of using key factors, but not the inherently subjective processing of post text itself, to identify deceptive online content. This paper presents initial work on a potential deceptive content detection tool and also, through the networks that it presents for this purpose, considers the interrelationships of factors that can be used to determine whether a post is deceptive content or not and their comparative importance.
format	article
author	Xinyu (Sherwin) Liang Jeremy Straub
author_facet	Xinyu (Sherwin) Liang Jeremy Straub
author_sort	Xinyu (Sherwin) Liang
title	Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System
title_short	Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System
title_full	Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System
title_fullStr	Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System
title_full_unstemmed	Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System
title_sort	deceptive online content detection using only message characteristics and a machine learning trained expert system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a73b553bc4b14367b11ba7d897bf5470
work_keys_str_mv	AT xinyusherwinliang deceptiveonlinecontentdetectionusingonlymessagecharacteristicsandamachinelearningtrainedexpertsystem AT jeremystraub deceptiveonlinecontentdetectionusingonlymessagecharacteristicsandamachinelearningtrainedexpertsystem
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Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System

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