Open Access

ARTICLE

AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors

by Shihao Zhou¹, Ziyuan Xu^3,4, Jin Han^1,*, Xingming Sun^1,2, Yi Cao⁵

1 Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, 201144, China
3 Nanjing University (Suzhou) High and New Technology Research Institute, Suzhou, 215123, China
4 Jiangsu Union Technical Institute, Wuxi, 214145, China
5 Department of Electrical and Computer Engineering, University of Windsor, ON, N9B 3P4, Canada

* Corresponding Author: Jin Han. Email:

Computers, Materials & Continua 2022, 71(3), 5995-6009. https://doi.org/10.32604/cmc.2022.023316

Received 03 September 2021; Accepted 08 November 2021; Issue published 14 January 2022

Abstract

In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars’ submission behaviors. The model can effectively capture the atypical behavioral approach and operation of the author. As such, it is able to detect various types of misconduct, thereby improving the accuracy of detection when combined with a text content analysis. The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct. First, the effect of scholars’ behavioral features on the model are considered and analyzed. Second, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars. Finally, the text features of the papers are combined with the scholars’ behavioral data to improve recognition precision. Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.

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Keywords

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