大语言模型时代基于文本可信度信号的自动可信度评估综述
A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models
AI总结：
本综述回顾175篇文献，系统梳理了NLP领域内九类文本可信度信号的检测与自动评估方法，深入分析事实性、说服技巧等关键类别，并指出大语言模型时代下的未来研究方向与挑战。
摘要：
在社交媒体和生成式AI时代，自动评估在线内容可信度的能力变得日益关键，这是对传统虚假信息检测方法的补充。可信度评估依赖于将各种不同的可信度信号（如内容主观性、偏见或说服技巧的存在等小信息单元）聚合为一个最终的可信度标签或分数。然而，当前关于自动可信度评估和可信度信号检测的研究仍然高度碎片化，许多信号孤立研究且缺乏整合。值得注意的是，同时检测和聚合多种可信度信号的方法十分稀缺。由于缺乏一个全面且最新的研究概述来将这些研究工作置于一个共同框架下并识别共同趋势、挑战和开放问题，这些挑战进一步加剧。在本综述中，我们通过系统全面地回顾175篇研究论文来填补这一空白，重点关注自然语言处理（NLP）领域内的文本可信度信号，该领域正因大语言模型（LLMs）的进步而经历快速变革。在将NLP研究置于更广泛的多学科背景下的同时，我们考察了自动可信度评估方法以及九类可信度信号的检测。我们对三个关键类别进行了深入分析：1）事实性、主观性与偏见；2）说服技巧与逻辑谬误；3）值得核查与已被核查的声明。除了总结现有的方法、数据集和工具外，我们还概述了未来的研究方向和新兴机遇，并特别关注生成式AI带来的不断演变的挑战。
英文摘要：
In the age of social media and generative AI, the ability to automatically assess the credibility of online content has become increasingly critical, complementing traditional approaches to false information detection. Credibility assessment relies on aggregating diverse credibility signals - small units of information, such as content subjectivity, bias, or a presence of persuasion techniques - into a final credibility label/score. However, current research in automatic credibility assessment and credibility signals detection remains highly fragmented, with many signals studied in isolation and lacking integration. Notably, there is a scarcity of approaches that detect and aggregate multiple credibility signals simultaneously. These challenges are further exacerbated by the absence of a comprehensive and up-to-date overview of research works that connects these research efforts under a common framework and identifies shared trends, challenges, and open problems. In this survey, we address this gap by presenting a systematic and comprehensive literature review of 175 research papers, focusing on textual credibility signals within the field of Natural Language Processing (NLP), which undergoes a rapid transformation due to advancements in Large Language Models (LLMs). While positioning the NLP research into the the broader multidisciplinary landscape, we examine both automatic credibility assessment methods as well as the detection of nine categories of credibility signals. We provide an in-depth analysis of three key categories: 1) factuality, subjectivity and bias, 2) persuasion techniques and logical fallacies, and 3) check-worthy and fact-checked claims. In addition to summarising existing methods, datasets, and tools, we outline future research direction and emerging opportunities, with particular attention to evolving challenges posed by generative AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
