A comparison of artificial intelligence models used for fake news detection

This article aims to compare current state-of-the-art natural language processing models (NLP) fine-tuned for fake news detection based on a set of metrics and asses their effectiveness as a part of a disinformation management structure. The need for a development of this area comes as a response t...

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Main Authors: Ștefan Emil REPEDE, Remus BRAD
Format: Article
Language:English
Published: Carol I National Defence University Publishing House 2023-04-01
Series:Bulletin of "Carol I" National Defense University
Subjects:
Online Access:https://revista.unap.ro/index.php/bulletin/article/view/1680
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author Ștefan Emil REPEDE
Remus BRAD
author_facet Ștefan Emil REPEDE
Remus BRAD
author_sort Ștefan Emil REPEDE
collection DOAJ
description This article aims to compare current state-of-the-art natural language processing models (NLP) fine-tuned for fake news detection based on a set of metrics and asses their effectiveness as a part of a disinformation management structure. The need for a development of this area comes as a response to the overwhelming and unregulated spread of fake news that represents one of the current major difficulties in today`s era. The development of AI technologies has a direct impact over the creation and spreading of misinformation and disinformation as a result of the multiple uses that technology may have. Currently, machine learning techniques are used for the development of large language models (LLM). These developments in science are also used in disinformation campaigns. Related to this matter the concept of disinformation management has arisen as a cybersecurity issue integral in the current cyber threat landscape
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spelling doaj.art-e0b44a872ebb40e08a0cda7ab71dd7ba2024-01-31T13:15:44ZengCarol I National Defence University Publishing HouseBulletin of "Carol I" National Defense University2284-936X2284-93782023-04-0112110.53477/2284-9378-23-10A comparison of artificial intelligence models used for fake news detectionȘtefan Emil REPEDE0Remus BRAD1Universitatea „Lucian Blaga” din Sibiu“Lucian Blaga” University, Sibiu This article aims to compare current state-of-the-art natural language processing models (NLP) fine-tuned for fake news detection based on a set of metrics and asses their effectiveness as a part of a disinformation management structure. The need for a development of this area comes as a response to the overwhelming and unregulated spread of fake news that represents one of the current major difficulties in today`s era. The development of AI technologies has a direct impact over the creation and spreading of misinformation and disinformation as a result of the multiple uses that technology may have. Currently, machine learning techniques are used for the development of large language models (LLM). These developments in science are also used in disinformation campaigns. Related to this matter the concept of disinformation management has arisen as a cybersecurity issue integral in the current cyber threat landscape https://revista.unap.ro/index.php/bulletin/article/view/1680fake news;misinformation;.disinformation management;natural language processing;NLP;artificial intelligence;
spellingShingle Ștefan Emil REPEDE
Remus BRAD
A comparison of artificial intelligence models used for fake news detection
Bulletin of "Carol I" National Defense University
fake news;
misinformation;.
disinformation management;
natural language processing;
NLP;
artificial intelligence;
title A comparison of artificial intelligence models used for fake news detection
title_full A comparison of artificial intelligence models used for fake news detection
title_fullStr A comparison of artificial intelligence models used for fake news detection
title_full_unstemmed A comparison of artificial intelligence models used for fake news detection
title_short A comparison of artificial intelligence models used for fake news detection
title_sort comparison of artificial intelligence models used for fake news detection
topic fake news;
misinformation;.
disinformation management;
natural language processing;
NLP;
artificial intelligence;
url https://revista.unap.ro/index.php/bulletin/article/view/1680
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