Author Profiling and Plagiarism Detection

Rosso, Paolo

doi:10.1007/978-3-319-25485-2_6

Cited by 2 publications

(3 citation statements)

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“…The findings were supported by some studies [24], [57], [80]. Finally, considerable efforts have been applied to detecting crosslanguage plagiarism, and although some authors [2]- [6], [8]- [10], [81] have proposed or identified techniques capable of detecting.…”

Section: B Discussionmentioning

confidence: 57%

“…Further, Franco-Salvador et al [5] examined the contributions of knowledge graphs to cross-language plagiarism detection in three areas: word meaning disambiguation, vocabulary extension, and representation through similarities to a collection of ideas; Ferrero et al [6] studied cross-language plagiarism detection techniques across six language pairings and two granularities of text units in order to reach solid findings of the best algorithms while also doing in-depth analyses of connections across document types and languages; and Tlitova et al [7]. reviewed the available techniques for detecting cross-language plagiarism in scientific publications, with a particular emphasis on the Russian-English language pair These studies attempted to provide quality information that can assist in the detection process [2], [3], [8]- [10]. Despite the authors' best efforts and different evidence-based recommendations for cross-language plagiarism detection, their implementation remains difficult due mostly to the differences in linguistic structures across languages.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Language Plagiarism Detection: Methods, Tools, and Challenges: A Systematic Review

Botto-Tobar¹,

Serebrenik²,

Brand³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Plagiarism is one of the most serious academic offenses. However, people have adopted different approaches to avoid plagiarism, such as transcribing excerpts from one language. Thus, it is challenging to realize this plagiarism form unless someone fully understands another language. Researchers have developed approaches for detecting plagiarism in a variety of different languages. However, most methods created in the past have proved effective for detecting plagiarism in papers published in a single language, most notably English. Therefore, this paper aims to provide a systematic literature review of cross-language plagiarism detection methods (CLPD) in a natural language context. The approach used to perform this study consisted of an extensive search for relevant literature through an SLR and Snowballing. Therefore, we present an overview of (i) cross-language plagiarism detection techniques; (ii) the artifacts and the aspects that were considered in the evaluation phase; and (iii) the lack of guidelines and tools for its implementation. Its contribution lies in its ability to highlight emerging cross-language plagiarism detection techniques trends. Further, we identify any of these techniques in other domains, for instance, software engineering.

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Section: B Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Cross-Language Plagiarism Detection: Methods, Tools, and Challenges: A Systematic Review

Botto-Tobar¹,

Serebrenik²,

Brand³

2022

International Journal on Advanced Science, Engineering and Information Technology

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“…The native language recognition problem touches upon various text classification tasks, such as author profiling [ 7 ], authorship attribution [ 8 ], programmer identification [ 9 ], review classification [ 10 ], and Twitter sentiment analysis [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Finding Dutch natives in online forums

Boom

Veenman

2018

Forensic Sciences Research

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Law enforcement agencies have a restricted area in which their powers apply, which is called their jurisdiction. These restrictions also apply to the Internet. However, on the Internet, the physical borders of the jurisdiction, typically country borders, are hard to discover. In our case, it is hard to establish whether someone involved in criminal online behavior is indeed a Dutch citizen. We propose a way to overcome the arduous task of manually investigating whether a user on an Internet forum is Dutch or not. More precisely, we aim to detect that a given English text is written by a Dutch native author. To develop a detector, we follow a machine learning approach. Therefore, we need to prepare a specific training corpus. To obtain a corpus that is representative for online forums, we collected a large amount of English forum posts from Dutch and non-Dutch authors on Reddit. To learn a detection model, we used a bag-of-words representation to capture potential misspellings, grammatical errors or unusual turns of phrases that are characteristic of the mother tongue of the authors. For this learning task, we compare the linear support vector machine and regularized logistic regression using the appropriate performance metrics f 1 score, precision, and average precision. Our results show logistic regression with frequency-based feature selection performs best at predicting Dutch natives. Further study should be directed to the general applicability of the results that is to find out if the developed models are applicable to other forums with comparable high performance.

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Author Profiling and Plagiarism Detection

Abstract: In this paper we introduce the topics that we will cover in

Cited by 2 publications

References 29 publications

Cross-Language Plagiarism Detection: Methods, Tools, and Challenges: A Systematic Review

Cross-Language Plagiarism Detection: Methods, Tools, and Challenges: A Systematic Review

Finding Dutch natives in online forums

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