The rapid expansion of human-software-agent interaction has come with new issues. Accordingly, different engage-ments are necessary to adapt to changing human needs in dynamic socio-technical systems. Generally, cybervandalism is the act of leaving any negative impact on any piece of writing in an attempt to modify it. In Wikipedia, vandalism is any attempt to modify an article in a way that negatively affects the article's quality. Recently, several automatic detec-tion techniques and related features have been developed to address this issue. This work introduces a deep learning model with a new and light architecture to detect vandalism in Wikipedia articles. The proposed model employs a one-dimensional convolutional neural network architecture (1D CNN) that can determine the type of modification in Wikipedia articles based on two main stages: the feature extraction stage and the vandalism detection stage, preceded by the data-resampling step, which is used to address class imbalance issues in the dataset. Features are extracted from edits and their associated metadata, as well as new features (reviewers' trust), and then only the salient features are adopted to make a decision about the article; regular or vandalism can contribute to improving the accuracy of predic-tion. The experiments were conducted on a benchmark dataset, the PAN-WVC-2010 corpus, taken from a vandalism detection competition hosted at the CLEF conference. The proposed system, with the new features added, has achieved an accuracy of 100%.
Ali, Azha Talal Mohammed; Hallawi, Huda; and Al-Shakarchy, Noor D.
"CyberVandalism Detection in Wikipedia Using Light Architecture of 1D-CNN,"
Karbala International Journal of Modern Science: Vol. 9
, Article 3.
Available at: https://doi.org/10.33640/2405-609X.3321
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.