Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today.
In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies.
Fact-Checking Complex Claims with Program-Guided Reasoning
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）
Technische Universität Darmstadt（达姆施塔特工业大学）；University of Tokyo（东京大学）
Peer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial rev…
Technical University of Darmstadt（达姆施塔特工业大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；IBM Research Europe（IBM欧洲研究院）
Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available. Despite increasing interest in automatic fact-checking, it is still unclear if automated approaches can realistically refute harmful real-world misinformation. Here, we contrast and compare NLP fact-checking with how professional fact-checkers combat misinformation in the absence of counter-evidence. In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot re…
To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participants were free to group the given consequences of chosen cyber attacks into as many categories as they wished using rationales they see fit. The results of the open card sorting study showed a large amount of inter-participant variation making the research team wonder how the consequences of security attacks were comprehended by the participants. As an exploration of whether it is possible to explain user's mental model…
Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity
Department of Computer Science and Engineering, Dhaka International University（达卡国际大学计算机科学与工程系）；Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology（孟加拉国工程技术大学计算机科学与工程系）
Queen Mary University of London（伦敦玛丽女王大学）；Taif University（塔伊夫大学）
As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.
Tableau Research & User Research（Tableau研究与用户研究部）；Salesforce Research（赛富时研究院）；Hugging Face（抱抱脸）
Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML). However, the documentation intended to convey the model's details and appropriate use is tailored primarily to individuals with ML or NLP expertise. To address this gap, we conduct a design inquiry into interactive model cards, which augment traditionally static model cards with affordances for exploring model documentation and interacting with the models themselves. Our investigation consists of an initial conceptual study with e…
Transparency as Architecture: Structural Compliance Gaps in EU AI Act Article 50 II
Technichal University Berlin XpaliNLP Group（柏林工业大学 XpaliNLP 组）；Research Center for Artificial Intelligence (DFKI)（人工智能研究中心（DFKI））；Center for European Research on Trusted AI (CERTAIN)（欧洲可信人工智能研究中心（CERTAIN））
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；Cardiff University（卡迪夫大学）；Queen Mary University of London（伦敦女王学院）
This dissertation proposes a framework of user-centered security in Natural Language Processing (NLP), and demonstrates how it can improve the accessibility of related research. Accordingly, it focuses on two security domains within NLP with great public interest. First, that of author profiling, which can be employed to compromise online privacy through invasive inferences. Without access and detailed insight into these models' predictions, there is no reasonable heuristic by which Internet users might defend themselves from such inferences. Secondly, that of cyberbullying detection, which by…
Hongyi Honor College, Wuhan University（武汉大学弘毅学堂）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, …
The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in a variety of disciplines including natural language processing, machine learning, knowledge representation, databases, and journalism. While there has been substantial progress, relevant papers and articles have been published in research communities that are often unaware of each other and use inconsistent terminology, thus impeding understanding and further progress. In this paper we survey automated…
Center for Artificial Intelligence in Public Health Research (ZKI-PH)（公共健康研究中的人工智能研究中心）；Robert Koch Institute（罗伯特· Koch 研究所）；Department of Mathematics and Computer Science（数学与计算机科学系）
Automatic News Generation and Fact-Checking System Based on Language Processing
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Northeastern University（东北大学）；New York University（纽约大学）；University of California（加利福尼亚大学）；Carnegie Mellon University（卡内基梅隆大学）
Over the past decade, fake news and misinformation have turned into a major problem that has impacted different aspects of our lives, including politics and public health. Inspired by natural human behavior, we present an approach that automates the detection of fake news. Natural human behavior is to cross-check new information with reliable sources. We use Natural Language Processing (NLP) and build a machine learning (ML) model that automates the process of cross-checking new information with a set of predefined reliable sources. We implement this for Twitter and build a model that flags fa…
Annotator-Centric Active Learning for Subjective NLP Tasks
Idiap Research Institute（伊迪亚普研究所）；Leiden Institute of Advanced Computer Science, Leiden University（莱顿大学莱顿高级计算机科学学院）；Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）；Interactive Intelligence, TU Delft（代尔夫特理工大学交互智能研究所）
Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation
Applied Artificial Intelligence and Intelligent Systems (AAIINS) Laboratory（应用人工智能与智能系统实验室）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Data Science and Artificial Intelligence（数据科学与人工智能系）；Department of Software Engineering（软件工程系）；Faculty of Science and Information Technology（科学与信息技术学院）；Faculty of Science and Technology（科学与技术学院）
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Alibaba Group（阿里巴巴集团）；Baidu Inc.（百度公司）；OPPO Research Institute（OPPO研究院）；Sun-Yat Sen University（中山大学）
SciCom Wiki: Fact-Checking and FAIR Knowledge Distribution for Scientific Videos and Podcasts
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Cluster of Excellence SE²A – Sustainable and Energy-Efficient Aviation（卓越中心SE²A——可持续和节能航空）；TIB - Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心）
本文提出以 Wikibase 为核心的 SciCom Wiki 平台，并结合神经符号事实核查与知识图谱，支持科学视频和播客的 FAIR 表示、协作策展及内容核验。
