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- W3082105611 abstract "Malicious posts from a social media account by an unauthorized user could have severe effects for the account holder, such as the loss of a job or damage to their reputation. In this work, we consider an authorship verification task to detect unauthorized malicious social media posts. We propose a novel approach for authorship verification based on personalized, i.e., user-tailored, language models. We evaluate our proposed approach against a previous approach based on word embeddings and a one-class SVM. A large amount of text might not necessarily be available for an individual social media user. We therefore demonstrate that our proposed approach out-performs previous approaches, while requiring orders of magnitude less user-specific training text." @default.
- W3082105611 created "2020-09-08" @default.
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- W3082105611 date "2020-01-01" @default.
- W3082105611 modified "2023-10-14" @default.
- W3082105611 title "Authorship Verification with Personalized Language Models" @default.
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- W3082105611 doi "https://doi.org/10.1007/978-3-030-58323-1_27" @default.
- W3082105611 hasPublicationYear "2020" @default.
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