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- W4287890651 abstract "The increasingly anonymous methods people use to communicate in the modern world allow for more freedom of speech. The safety of anonymity, however, can enable criminals to cause harm to others through various means, such as blackmail, verbal abuse, threat letters and numerous other ways. These culprits, often hiding behind computer screens, can be extremely difficult to identify and especially difficult to find definitive proof of their wrongdoings. They are not completely untraceable, however, as they are bound to leave clues in the text, linking it to them. The way they phrase sentences, the words they use, how often they use them and other parts of their idiolect can be used to identify them and even connect them to other texts. Through analyzing the text, it becomes possible to catch these individuals. This analysis is neither simple nor cheap, the aid of linguistic experts is critical, and even they are likely to encounter difficulties. This article explores the way in which the work of such experts can be assisted through computer analysis based on machine learning techniques and the role Artificial Intelligence plays in bringing these criminals to justice. Our current paper investigates how linguistic features can be automatically extracted to be used in the field. Through a total of 61 real text artefacts written in the Hungarian language by four different individuals, we extract various syntactic and semantic linguistic features which reflect the author’s idiolect and aid the expert’s work. We demonstrate how the technique can aid author identification in criminology." @default.
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- W4287890651 date "2022-01-01" @default.
- W4287890651 modified "2023-10-18" @default.
- W4287890651 title "Computer-Aided Forensic Authorship Identification in Criminology" @default.
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- W4287890651 doi "https://doi.org/10.1007/978-3-031-10548-7_42" @default.
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