Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Authorship verification as a one-class classification problem
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Measuring Differentiability: Unmasking Pseudonymous Authors
The Journal of Machine Learning Research
Foundations and Trends in Information Retrieval
Computational methods in authorship attribution
Journal of the American Society for Information Science and Technology
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Language Resources and Evaluation
Authorship attribution in the wild
Language Resources and Evaluation
Predicting age and gender in online social networks
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity
ACM Transactions on Information and System Security (TISSEC)
Automatic knowledge extraction from documents
IBM Journal of Research and Development
Hi-index | 0.00 |
Computational stylometry, as in authorship attribution or profiling, has a large potential for applications in diverse areas: literary science, forensics, language psychology, sociolinguistics, even medical diagnosis. Yet, many of the basic research questions of this field are not studied systematically or even at all. In this paper we will go into these problems, and suggest that a reinterpretation of current and historical methods in the framework and methodology of machine learning of natural language processing would be helpful. We also argue for more attention in research for explanation in computational stylometry as opposed to purely quantitative evaluation measures and propose a strategy for data collection and analysis for achieving progress in computational stylometry. We also introduce a fairly new application of computational stylometry in internet security.