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Detecting malicious tweets in trending topics using a statistical analysis of language

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dc.contributor.authorMartínez Romo, Juan
dc.contributor.authorAraujo Serna, M. Lourdes
dc.date.accessioned2024-05-21T13:03:28Z
dc.date.available2024-05-21T13:03:28Z
dc.date.issued2013-06-01
dc.description.abstractTwitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe- art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.es
dc.description.versionversión publicada
dc.identifier.doi10.1016/j.eswa.2012.12.015
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/20.500.14468/19982
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsspam detection
dc.subject.keywordssocial network
dc.subject.keywordsstatistical natural language processing
dc.subject.keywordsmachine learning
dc.titleDetecting malicious tweets in trending topics using a statistical analysis of languagees
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication91b7e317-2a30-494f-98e9-3a0e026747b1
relation.isAuthorOfPublication77c4023e-4374-442a-9dfb-b9d4b609c31e
relation.isAuthorOfPublication.latestForDiscovery91b7e317-2a30-494f-98e9-3a0e026747b1
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