doi: 10.4304/jsw.9.1.11-19
CWAAP: An Authorship Attribution Forensic Platform for Chinese Web Information
- 1 College of Information Science and Technology, Agricultural University of Hebei, Baoding, China
- 2 College of Economic and Trade, Agricultural University of Hebei, Baoding, China
Abstract
Illegal web information is common on the Internet. To prevent phenomena of illegal web information from happening, providing effective evidence for court to punish the criminals by means of law is one effective method. In this paper, an authorship attribution platform for Chinese web information, CWAAP, is described. Based on the language characteristics of Chinese web information, lexical features and structural features which can express the author’s writing habit are extracted. Support vector machines (SVM) are used for learning author’s writing features. To test the effectiveness of CWAAP, literature, Blog and BBS datasets are used in the experiments on the platform. Five experiments are performed. Experimental results show that lexical features and structural features are effective. The number of words in training samples should exceed 200 at least. By Information Gain feature selection methods, 800 lexical features can express the authors’ writing style. There is a small difference between the authors’ topics. All the parts of speech reserved are perfect. These results confirm that the platform is effective and feasible for cybercrime forensic.
Keywords
- CWAAP
- Authorship attribution
- Forensic
How to Cite
Jianbin Ma, Ying Li, and Guifa Teng, "CWAAP: An Authorship Attribution Forensic Platform for Chinese Web Information," Journal of Software, vol. 9, no. 1, pp. 11-19, 2014. https://doi.org/ 10.4304/jsw.9.1.11-19
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).