论文标题
独立于领域的欺骗：定义，分类学和语言提示辩论
Domain-Independent Deception: Definition, Taxonomy and the Linguistic Cues Debate
论文作者
论文摘要
基于互联网的经济体和社会陷入欺骗性攻击中。这些攻击采用多种形式，例如假新闻，网络钓鱼和工作骗局，我们称之为“欺骗领域”。机器学习和自然处理的研究人员一直试图通过设计特定于域的探测器来改善这种不稳定的情况。最近只有几部作品考虑了与域无关的欺骗。我们收集了这些不同的研究线，并研究了沿着四个维度的独立欺骗。首先，我们为欺骗提供了新的计算定义，并使用概率理论将其正式化。其次，我们将欺骗分为新的分类法。第三，我们分析了有关系统评价的欺骗和供应指南的语言线索辩论。第四，我们提供了一些与领域无关的欺骗检测的证据和一些建议。
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 along four dimensions. First, we provide a new computational definition of deception and formalize it using probability theory. Second, we break down deception into a new taxonomy. Third, we analyze the debate on linguistic cues for deception and supply guidelines for systematic reviews. Fourth, we provide some evidence and some suggestions for domain-independent deception detection.
