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.
LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference
MaiNLP, Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心 MaiNLP）；Munich Center for Machine Learning（慕尼黑机器学习中心）；FNRS, CENTAL, UCLouvain（比利时法语区科学研究基金、鲁汶大学自然语言处理中心）；Faculty of Computer Science and UniVie Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学学院与维也纳大学计算机科学博士学院）
What if Deception Cannot be Detected? A Cross-Linguistic Study on the Limits of Deception Detection from Text
Institut für Maschinelle Sprachverarbeitung, University of Stuttgart, Germany（机器语言处理研究所，斯图加特大学，德国）；Fundamentals of Natural Language Processing, University of Bamberg, Germany（自然语言处理基础， Bamberg大学，德国）；Leibniz Institute for Psychology, Trier, Germany（莱布尼茨心理学研究所，特里尔，德国）
Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation
Learning, Data and Robotics (LDR) ESIEA Lab（学习、数据与机器人（LDR）ESIEA实验室）；Department of Computer Engineering（计算机工程系）；Graduate School of Management and Innovation（管理与创新研究生院）；Faculty of Science（科学学院）；Lebanese University（黎巴嫩大学）
Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks
University of Michigan（密歇根大学）；Columbia University（哥伦比亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Saarland University of Applied Science（萨尔应用科学大学）
Explainable Verbal Deception Detection using Transformers
National Technical University of Athens（雅典国立技术大学）；GESIS – Leibniz Institute for the Social Science（莱布尼茨社会科学研究所）；University College London（伦敦大学学院）；Tilburg University（蒂尔堡大学）
A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Uses
School of Computer Science and Technology（计算机科学与技术学院）；Beijing Jiaotong University（北京交通大学）；Department of Computer Science（计算机科学系）；Aalborg University（奥尔堡大学）
