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- W2975786635 abstract "Abstract Over the last few decades, there has been tremendous growth in online communication through different types of media. Communication via the Internet is anonymous, which causes a critical issue regarding identity tracing. Authorship identification can apply to tasks such as identifying an anonymous author, detecting plagiarism, or finding a ghostwriter. Previous research has outlined the various methods and their improvements for the identification of anonymous authors based on stylometry. However, changes in the writing style of an author over a long period has not been addressed. In this article, we propose a methodology for author identification where the writing style of an author changes. The proposed methodology consists of two phases: the first will show the change in writing style of the author and in another phase the change is mitigated by a new feature normalization technique. A novel Transform Feature to Current Time function is proposed for normalization, where features are shifted to current time and made available for further classification. A machine-learning algorithm is used to identify an author candidate. The experiments of the proposed methodology conducted on a set of text samples by several authors were collected over a different time period and the results show an improvement in performance." @default.
- W2975786635 created "2019-10-03" @default.
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- W2975786635 date "2019-09-23" @default.
- W2975786635 modified "2023-09-27" @default.
- W2975786635 title "Author identification with feature transformation method" @default.
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- W2975786635 doi "https://doi.org/10.1093/llc/fqz052" @default.
- W2975786635 hasPublicationYear "2019" @default.
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