Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)

Abstract—Social media are increasingly being used as sources in mainstream news coverage. However, since news is so rapidly updating it is very easy to fall into the trap of believing everything as truth. Spam content usually refers to the information that goes viral and skews users views on subject...

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Detalles Bibliográficos
Autores principales:	Tur, Georvic, Homsi, Masun Nabhan
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2017
Materias:	Ciencias Informáticas spam classification twitter topic discovering cost-sensitive classifier random forest
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/63208 http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/SLMDI/SLMDI-07.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-63208
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas spam classification twitter topic discovering cost-sensitive classifier random forest
spellingShingle	Ciencias Informáticas spam classification twitter topic discovering cost-sensitive classifier random forest Tur, Georvic Homsi, Masun Nabhan Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)
topic_facet	Ciencias Informáticas spam classification twitter topic discovering cost-sensitive classifier random forest
description	Abstract—Social media are increasingly being used as sources in mainstream news coverage. However, since news is so rapidly updating it is very easy to fall into the trap of believing everything as truth. Spam content usually refers to the information that goes viral and skews users views on subjects. Despite recent advances in spam analysis methods, it is still a challenging task to extract accurate and useful information from tweets. This paper aims at introducing a new approach for classification of spam and non-spam tweets using Cost-Sensitive Classifier that includes Random Forest. The approach consisted of three phases: preprocessing, classification and evaluation. In the preprocessing phase, tweets were first annotated manually and then four different sets of features were extracted from them. In the classification phase, four machine learning algorithms were first cross-validated aiming at determining the best base classifier for spam detection. Then, class imbalanced problem was dealt by resampling and incorporating arbitrary misclassification costs into the learning process. In the evaluation phase, the trained algorithm was tested with unseen tweets. Experimental results showed that the proposed approach helped mitigate overfitting and reduced classification error by achieving an overall accuracy of 89.14% in training and 76.82% in testing.
format	Objeto de conferencia Objeto de conferencia
author	Tur, Georvic Homsi, Masun Nabhan
author_facet	Tur, Georvic Homsi, Masun Nabhan
author_sort	Tur, Georvic
title	Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)
title_short	Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)
title_full	Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)
title_fullStr	Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)
title_full_unstemmed	Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)
title_sort	cost-sensitive classifier for spam detection on news media twitter accounts (revised april 2017)
publishDate	2017
url	http://sedici.unlp.edu.ar/handle/10915/63208 http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/SLMDI/SLMDI-07.pdf
work_keys_str_mv	AT turgeorvic costsensitiveclassifierforspamdetectiononnewsmediatwitteraccountsrevisedapril2017 AT homsimasunnabhan costsensitiveclassifierforspamdetectiononnewsmediatwitteraccountsrevisedapril2017
bdutipo_str	Repositorios
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Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts (revised April 2017)

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