AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors

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.