Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach

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dc.contributor.author Halawi, Bahia
dc.contributor.author Mourad, Azzam
dc.contributor.author Otrok, Hadi
dc.contributor.author Damiani, Ernesto
dc.date.accessioned 2021-04-15T19:26:15Z
dc.date.available 2021-04-15T19:26:15Z
dc.date.copyright 2018 en_US
dc.date.issued 2021-04-15
dc.identifier.issn 2169-3536 en_US
dc.identifier.uri http://hdl.handle.net/10725/12699
dc.description.abstract Due to the high popularity of Twitter, spammers tend to favor its use in spreading their commercial messages. In the context of detecting twitter spams, different statistical and behavioral analysis approaches were proposed. However, these techniques suffer from many limitations due to: 1) ongoing changes to Twitter's streaming API which constrains access to a user's list of followers/followees; 2) spammer's creativity in building diverse messages; 3) use of embedded links and new accounts; and 4) need for analyzing different characteristics about users without their consent. To address the aforementioned challenges, we propose a novel ontology-based approach for spam detection over Twitter during events by analyzing the relationship between ham user tweets versus spams. Our approach relies solely on public tweet messages while performing the analysis and classification tasks. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Similarity ratio among the dictionary and tweets is used to reflect the legitimacy of the messages. Experiments conducted on real tweet data illustrate that message-to-message techniques achieved a low detection rate compared with our ontology-based approach which outperforms them by approximately 200%, in addition to promising scalability for large data analysis. en_US
dc.language.iso en en_US
dc.title Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SAS en_US
dc.author.idnumber 200904853 en_US
dc.author.department Computer Science And Mathematics en_US
dc.relation.journal IEEE Access en_US
dc.journal.volume 6 en_US
dc.article.pages 63890 - 63904 en_US
dc.keywords Twitter en_US
dc.keywords Ontologies en_US
dc.keywords Electronic mail en_US
dc.keywords Feature extraction en_US
dc.keywords Uniform resource locators en_US
dc.keywords Tagging en_US
dc.keywords Analytical models en_US
dc.identifier.doi https://doi.org/10.1109/ACCESS.2018.2877685 en_US
dc.identifier.ctation Halawi, B., Mourad, A., Otrok, H., & Damiani, E. (2018). Few are as good as many: An ontology-based tweet spam detection approach. IEEE Access, 6, 63890-63904. en_US
dc.author.email azzam.mourad@lau.edu.lb
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://ieeexplore.ieee.org/abstract/document/8502923 en_US
dc.orcid.id https://orcid.org/0000-0001-9434-5322 en_US
dc.author.affiliation Lebanese American University en_US

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