Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms

Nowadays the problem of fake news in social media is dramatically increasing, especially when it refers to fake news about Covid-19, as it is a recent and global problem. Because of this fact, it is important to have the ability to detect and delete such news immediately. In our research we concentr...

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Autores principales:	Elena Shushkevich, John Cardiff
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	fake news covid-19 scikit learn gmdh shell Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/5a817641a773460781bd4d9d04794b8b
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spelling	oai:doaj.org-article:5a817641a773460781bd4d9d04794b8b2021-11-20T15:59:33ZDetecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms2305-72542343-073710.23919/FRUCT53335.2021.9599970https://doaj.org/article/5a817641a773460781bd4d9d04794b8b2021-10-01T00:00:00Zhttps://www.fruct.org/publications/fruct30/files/Shu.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737Nowadays the problem of fake news in social media is dramatically increasing, especially when it refers to fake news about Covid-19, as it is a recent and global problem. Because of this fact, it is important to have the ability to detect and delete such news immediately. In our research we concentrate our efforts on detecting fake news about Coronavirus on small datasets, using the Constraint-2021 corpus: the full dataset (10,700 messages) and the limited dataset (1,000 messages). We compare classical Machine Learning Algorithms (4 algorithms: Logistic Regression, Support Vectors Machine, Gradient Boosting, Random Forest) algorithms of classification from the Scikit-learn library, GMDH-Shell tool (2 algorithms: Combi and Neuro), and Deep Neural Network (LSTM model). The results show that GMDH algorithms outperform traditional Machine Learning Algorithms and are comparable with Neural Networks models results on the limited dataset.Elena ShushkevichJohn CardiffFRUCTarticlefake newscovid-19scikit learngmdh shellTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 1, Pp 253-258 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fake news covid-19 scikit learn gmdh shell Telecommunication TK5101-6720
spellingShingle	fake news covid-19 scikit learn gmdh shell Telecommunication TK5101-6720 Elena Shushkevich John Cardiff Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms
description	Nowadays the problem of fake news in social media is dramatically increasing, especially when it refers to fake news about Covid-19, as it is a recent and global problem. Because of this fact, it is important to have the ability to detect and delete such news immediately. In our research we concentrate our efforts on detecting fake news about Coronavirus on small datasets, using the Constraint-2021 corpus: the full dataset (10,700 messages) and the limited dataset (1,000 messages). We compare classical Machine Learning Algorithms (4 algorithms: Logistic Regression, Support Vectors Machine, Gradient Boosting, Random Forest) algorithms of classification from the Scikit-learn library, GMDH-Shell tool (2 algorithms: Combi and Neuro), and Deep Neural Network (LSTM model). The results show that GMDH algorithms outperform traditional Machine Learning Algorithms and are comparable with Neural Networks models results on the limited dataset.
format	article
author	Elena Shushkevich John Cardiff
author_facet	Elena Shushkevich John Cardiff
author_sort	Elena Shushkevich
title	Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms
title_short	Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms
title_full	Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms
title_fullStr	Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms
title_full_unstemmed	Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms
title_sort	detecting fake news about covid-19 on small datasets with machine learning algorithms
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/5a817641a773460781bd4d9d04794b8b
work_keys_str_mv	AT elenashushkevich detectingfakenewsaboutcovid19onsmalldatasetswithmachinelearningalgorithms AT johncardiff detectingfakenewsaboutcovid19onsmalldatasetswithmachinelearningalgorithms
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Detecting Fake News About Covid-19 on Small Datasets with Machine Learning Algorithms

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