The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research aiming at automating the process. Interestingly, previous work has largely ignored the growing number of claims about images. This is despite the fact that visual imagery is more influential than text and naturally appears alongside fake news. Here we aim at bridging this gap. In particular, we create a new dataset for this problem, and we explore a variety of features modeling the claim, the image, and the relationship between the claim and the image. The evaluation results show sizable improvements over the baseline. We release our dataset, hoping to enable further research on fact-checking claims about images.
Imperial College London（帝国理工学院）；Department of Computing（计算机系）
Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, …
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
King’s College London（伦敦国王学院）；University of Utah（犹他大学）；University of Pennsylvania（宾夕法尼亚大学）；TIB – Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心（TIB））
VILLAIN at AVerImaTeC: Verifying Image-Text Claims via Multi-Agent Collaboration
School of AI Convergence, Soongsil University（人工智能融合学院，顺世大学）；MAUM AI Inc.（MAUM人工智能公司）；Department of Intelligent Semiconductors, Soongsil University（智能半导体系，顺世大学）
Sofia University “St. Kliment Ohridski”（圣基里尔·奥赫里德斯基索菲亚大学）；HSE University（高等经济大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
While there has been substantial progress in developing systems to automate fact-checking, they still lack credibility in the eyes of the users. Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision. This is a sensible approach as people trust manual fact-checking, and as many claims are repeated multiple times. Yet, a major issue when building such systems is the small number of known tweet--verifying article pair…
While recent work on automated fact-checking has focused mainly on verifying and explaining claims, for which the list of claims is readily available, identifying check-worthy claim sentences from a text remains challenging. Current claim identification models rely on manual annotations for each sentence in the text, which is an expensive task and challenging to conduct on a frequent basis across multiple domains. This paper explores methodology to identify check-worthy claim sentences from fake news articles, irrespective of domain, without explicit sentence-level annotations. We leverage two…
Fact-checking requires retrieving evidence related to a claim under investigation. The task can be formulated as question generation based on a claim, followed by question answering. However, recent question generation approaches assume that the answer is known and typically contained in a passage given as input, whereas such passages are what is being sought when verifying a claim. In this paper, we present {\it Varifocal}, a method that generates questions based on different focal points within a given claim, i.e.\ different spans of the claim and its metadata, such as its source and date. O…
Sun Yat-sen University（中山大学）；Microsoft Research（微软研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Microsoft Search Technology Center Asia（微软亚洲搜索技术中心）
Verifying the correctness of a textual statement requires not only semantic reasoning about the meaning of words, but also symbolic reasoning about logical operations like count, superlative, aggregation, etc. In this work, we propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking. It achieves the state-of-the-art performance on TABFACT, a large-scale, benchmark dataset built for verifying a textual statement with semi-structured tables. This is achieved by a graph module network built upon the Transformer-based architecture. With a tex…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data scarcity issue in other languages. The lack of fact-checking datasets in low-resource languages calls for an effective cross-lingual transfer technique for fact-checking. Additionally, trustworthy information in different languages can be complementary and helpful in verifying facts. To this end, we present the first fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from m…
Sun Yat-sen University（中山大学）；Peking University（北京大学）；Microsoft Research（微软研究院）
Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling. We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pr…
With the explosive growth of scientific publications, making the synthesis of scientific knowledge and fact checking becomes an increasingly complex task. In this paper, we propose a multi-task approach for verifying the scientific questions based on a joint reasoning from facts and evidence in research articles. We propose an intelligent combination of (1) an automatic information summarization and (2) a Boolean Question Answering which allows to generate an answer to a scientific question from only extracts obtained after summarization. Thus on a given topic, our proposed approach conducts s…
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
IIT Kharagpur（印度理工学院克勒格布尔分校）；Aditya English Medium School（阿迪蒂亚英语中学）；UCLA（加利福尼亚大学洛杉矶分校）；Stanford University（斯坦福大学）；University of South Carolina（南卡罗来纳大学）；Amazon GenAI（亚马逊生成式AI部门）
Saarland University（萨尔兰大学）；University of the Basque Country（巴斯克大学）；German Research Center for Artificial Intelligence, Saarland Informatics Campus（德国人工智能研究中心，萨尔兰信息学校区）；Faculty of Information Technology, Brno University of Technology（信息技术学院，布拉格技术大学）；Kempelen Institute of Intelligent Technologies（Kempelen智能技术研究所）
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Full Fact（Full Fact事实核查机构）；Qatar University（卡塔尔大学）；Università di Bologna（博洛尼亚大学）；EURECOM（欧洲通信学院）；University of Padova（帕多瓦大学）
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker -- a professional whose main aim is to examine claims u…
University of Washington（华盛顿大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that simple domain adaptation techniques substantially improve performance compared to models trained on Wikipedia or political news. We show t…
ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)（德国人工智能研究中心）；Technische Universität Berlin（柏林工业大学）；Leuphana Universität Lüneburg（吕讷堡大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
Universidad Politecnica de Madrid（马德里理工大学）；Universidad Rey Juan Carlos de Madrid（马德里胡安·卡洛斯国王大学）
Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidences about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking that can be used for either applications designed for the general public and by fact-checking orga…
The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet. We explore stories reported by fact-checking groups PolitiFact, Poynter and Snopes from January to June 2020, characterising them into six story clusters before then analyse time-series and story validity trends and the level of agreement across sites. We further break down the story clusters into more granular story types by proposing a unique automated method with a BERT classifier, which can be used to classify div…
