Domain-Independent Deception: A New Taxonomy and Linguistic Analysis

Authors: Rakesh M. Verma, Nachum Dershowitz, Victor Zeng, Dainis Boumber, Xuting Liu
Published: 2024-02-01

Source: https://arxiv.org/abs/2402.01019

PDF: https://arxiv.org/pdf/2402.01019

AIにより推定されたラベル

バイアス ドメイン非依存性 ウォーターマーキング

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

Abstract

Internet-based economies and societies are drowning in deceptive attacks. These attacks take many forms, such as fake news, phishing, and job scams, which we call “domains of deception.” Machine-learning and natural-language-processing researchers have been attempting to ameliorate this precarious situation by designing domain-specific detectors. Only a few recent works have considered domain-independent deception. We collect these disparate threads of research and investigate domain-independent deception. First, we provide a new computational definition of deception and break down deception into a new taxonomy. Then, we analyze the debate on linguistic cues for deception and supply guidelines for systematic reviews. Finally, we investigate common linguistic features and give evidence for knowledge transfer across different forms of deception.