Analyzing Mathematical Content to Detect Academic Plagiarism
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10366347" target="_blank" >RIV/00216208:11320/17:10366347 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/3132847.3133144" target="_blank" >http://dx.doi.org/10.1145/3132847.3133144</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3132847.3133144" target="_blank" >10.1145/3132847.3133144</a>
Alternative languages
Result language
angličtina
Original language name
Analyzing Mathematical Content to Detect Academic Plagiarism
Original language description
This paper presents, to our knowledge, the first study on analyzing mathematical expressions to detect academic plagiarism. We make the following contributions. First, we investigate confirmed cases of plagiarism to categorize the similarities of mathematical content commonly found in plagiarized publications. From this investigation, we derive possible feature selection and feature comparison strategies for developing math-based detection approaches and a ground truth for our experiments. Second, we create a test collection by embedding confirmed cases of plagiarism into the NTCIR-11 MathIR Task dataset, which contains approx. 60 million mathematical expressions in 105,120 documents from arXiv.org. Third, we develop a first math-based detection approach by implementing and evaluating different feature comparison approaches using an open source parallel data processing pipeline built using the Apache Flink framework. The best performing approach identifies all but two of our real-world test cases at the top rank and achieves a mean reciprocal rank of 0.86. The results show that mathematical expressions are promising text-independent features to identify academic plagiarism in large collections. To facilitate future research on math-based plagiarism detection, we make our source code and data available.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-22224S" target="_blank" >GA17-22224S: User preference analytics in multimedia exploration models</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
ISBN
978-1-4503-4918-5
ISSN
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e-ISSN
neuvedeno
Number of pages
4
Pages from-to
2211-2214
Publisher name
ACM
Place of publication
New York
Event location
Singapore
Event date
Nov 6, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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