Konstantinos F. Xylogiannopoulos, P. Karampelas, R. Alhajj
{"title":"Text Mining for Plagiarism Detection: Multivariate Pattern Detection for Recognition of Text Similarities","authors":"Konstantinos F. Xylogiannopoulos, P. Karampelas, R. Alhajj","doi":"10.1109/ASONAM.2018.8508265","DOIUrl":null,"url":null,"abstract":"The problem of plagiarism the recent years has been intensified by the availability of information in digital form and the accessibility of the electronic libraries through the Internet. As a result, plagiarism detection has been transformed into a big data analytics problem since the number of digital sources is extravagant and a new document needs to be compared with millions of other existing documents. In this paper, a text mining methodology is proposed that can detect all common patterns between a document and the documents in a reference database. The technique is based on a pattern detection algorithm and the corresponding data structure that enables the algorithm to detect all common patterns. The methodology has been applied in a well-defined dataset providing very promising results identifying difficult cases of plagiarism such as technical disguise.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASONAM.2018.8508265","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The problem of plagiarism the recent years has been intensified by the availability of information in digital form and the accessibility of the electronic libraries through the Internet. As a result, plagiarism detection has been transformed into a big data analytics problem since the number of digital sources is extravagant and a new document needs to be compared with millions of other existing documents. In this paper, a text mining methodology is proposed that can detect all common patterns between a document and the documents in a reference database. The technique is based on a pattern detection algorithm and the corresponding data structure that enables the algorithm to detect all common patterns. The methodology has been applied in a well-defined dataset providing very promising results identifying difficult cases of plagiarism such as technical disguise.