SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains
Pulchowk Campus, IoE（普尔乔克校区，工程学院（Institute of Engineering））；IIT Roorkee（鲁基印度理工学院）；State University of New York, Buffalo（纽约州立大学布法罗分校）；University of Wisconsin（威斯康星大学）；Cohere For AI Community（Cohere 人工智能社区）；University of Maryland（马里兰大学）
This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only identifying fraudulent financial content but also generating coherent, and concise explanations that clarify the rationale behind the classifications. Our approach achieved competitive results with an F1-score of 0.8283 for classification, and ROUGE-1 of 0.7253 for explanations. This work highlights the transformative potential of LLMs in financial applications, offering insights into their capabilities for combating misinformation and enhancing transparency while identifying areas for future improvement in robustness and domain adaptation.
All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection
University of Manchester（曼彻斯特大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Columbia University（哥伦比亚大学）；The FinAI；Halmstad University（哈尔姆斯塔德大学）；New York University（纽约大学）；University of Florida（佛罗里达大学）；National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
The University of Manchester（曼彻斯特大学）；Stevens Institute of Technology（史蒂文斯理工学院）；The Fin AI（Fin AI）；Columbia University（哥伦比亚大学）；Islamic University of Technology（伊斯兰科技大学）；Athens University of Economics and Business（雅典经济与商业大学）；Archimedes, Athena Research Center（阿基米德，亚特兰蒂斯研究中心）；MBZUAI；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Dubai Police（迪拜警察）；University of Melbourne（墨尔本大学）；Halmstad University（哈姆斯塔德大学）；University of Florida（佛罗里达大学）；ELLIS Manchester（曼彻斯特ELLIS）
In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-checking websites, and identified 9.4K relevant tweets to those claims. Tweets were manually-annotated by veracity to support research on misinformation detection, which is one of the major problems faced during a pandemic. ArCOV19-Rumors supports two levels of misinformation detection over Twitter: verifying free-text claims (called claim-level ver…
University of Warwick（华威大学）；King’s College London（伦敦国王学院）；Cranfield University（克兰菲尔德大学）；Queen Mary University of London（伦敦玛丽女王大学）；The Alan Turing Institute（阿兰·图灵研究所）
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment network…
Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. Training these machine learning models require datasets of sufficient scale, diversity and quality. However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformati…
An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verification
Yobe State University（约贝州立大学）；Yobe AI Research Laboratory (YAIR Lab)（约贝人工智能研究实验室）；Novosibirsk State University（新西伯利亚国立大学）；College of Computer Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学计算机科学与工程学院）
During the COVID-19 pandemic, social media platforms were ideal for communicating due to social isolation and quarantine. Also, it was the primary source of misinformation dissemination on a large scale, referred to as the infodemic. Therefore, automatic debunking misinformation is a crucial problem. To tackle this problem, we present two COVID-19 related misinformation datasets on Twitter and propose a misinformation detection system comprising network-based and content-based processes based on machine learning algorithms and NLP techniques. In the network-based process, we focus on social pr…
In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are not machine readable. This limits their applicability and adoption as they can only be used by a select expert audience in very specific settings. In this paper we propose an architecture based on …
Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models
Kempelen Institute of Intelligent Technologies（凯普伦智能技术研究所）；Faculty of Information Technology, Brno University of Technology（布尔诺理工大学信息技术学院）
False information has a significant negative influence on individuals as well as on the whole society. Especially in the current COVID-19 era, we witness an unprecedented growth of medical misinformation. To help tackle this problem with machine learning approaches, we are publishing a feature-rich dataset of approx. 317k medical news articles/blogs and 3.5k fact-checked claims. It also contains 573 manually and more than 51k automatically labelled mappings between claims and articles. Mappings consist of claim presence, i.e., whether a claim is contained in a given article, and article stance…
University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）；University of Michigan（密歇根大学）
Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved great successes in detecting misinformation. In this paper, we proposed an explainable natural language processing model based on DistilBERT and SHAP (Shapley Additive exPlanations) to combat misinformation about COVID-19 due to their efficiency an…
Social media contains unfiltered and unique information, which is potentially of great value, but, in the case of misinformation, can also do great harm. With regards to biomedical topics, false information can be particularly dangerous. Methods of automatic fact-checking and fake news detection address this problem, but have not been applied to the biomedical domain in social media yet. We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase). With this corpus, which we sample…
Lost in translation: using global fact-checks to measure multilingual misinformation prevalence, spread, and evolution
Department of Mathematical Modeling and Machine Learning, University of Zurich, Switzerland（苏黎世大学数学建模与机器学习系）；Digital Society Initiative, University of Zurich, Switzerland（苏黎世大学数字社会倡议）；Oxford Internet Institute, University of Oxford, United Kingdom（牛津大学网络研究所）；Meedan, San Francisco, United States（梅丹公司，旧金山，美国）
Following recent outbreaks, monkeypox-related misinformation continues to rapidly spread online. This negatively impacts response strategies and disproportionately harms LGBTQ+ communities in the short-term, and ultimately undermines the overall effectiveness of public health responses. In an attempt to combat monkeypox-related misinformation, we present PoxVerifi, an open-source, extensible tool that provides a comprehensive approach to assessing the accuracy of monkeypox related claims. Leveraging information from existing fact checking sources and published World Health Organization (WHO) i…
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；Hong Kong University of Science and Technology（香港科技大学）
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly. However, the usual supervised approach to debunking via misinformation classification requires human-annotated data and is not suited to the fast time-frame of newly emerging events such as the COVID-19 outbreak. In this paper, we postulate that misinformation itself has higher perplexity compared to truthful statements, and propose to leverage the perplexity to debunk false claims in an unsupervised manner. First, we extract reliable evidence from…
School of Interactive Computing（交互计算学院）；Georgia Institute of Technology（佐治亚理工学院）
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the largest corpus annotated with stance towards misinformation claims. The claims in Stanceosaurus originate from 15 fact-checking sources that cover diverse geographical regions and cultures. Unlike existing stance datasets, we introduce a more fine-grained 5-class labeling strategy with additional subcategories to distinguish implicit stance. Pre-trained transformer-based stance classifiers that are fine-tuned on our cor…
MIC: Explaining Image-Claim Inconsistencies in AI-Generated Multimodal Misinformation
KU Leuven（鲁汶大学）；TU Darmstadt（达姆施塔特工业大学）；National Research Center for Applied Cybersecurity ATHENE（国家应用网络安全研究中心ATHENE）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
TOBB University of Economics and Technology（TOBB经济与技术大学）
The massive amount of misinformation spreading on the Internet on a daily basis has enormous negative impacts on societies. Therefore, we need automated systems helping fact-checkers in the combat against misinformation. In this paper, we propose a model prioritizing the claims based on their check-worthiness. We use BERT model with additional features including domain-specific controversial topics, word embeddings, and others. In our experiments, we show that our proposed model outperforms all state-of-the-art models in both test collections of CLEF Check That! Lab in 2018 and 2019. We also c…
We introduce a FEVER-like dataset COVID-Fact of $4,086$ claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific cl…
While making investment decisions by reading financial documents, investors need to differentiate between in-claim and outof-claim numerals. In this paper, we present a tool which does it automatically. It extracts context embeddings of the numerals using one of the transformer based pre-trained language model called BERT. After this, it uses a Logistic Regression based model to detect whether the numerals is in-claim or out-of-claim. We use FinNum-3 (English) dataset to train our model. After conducting rigorous experiments we achieve a Macro F1 score of 0.8223 on the validation set. We have …
Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts. In this work, we frame health misinformation as a risk assessment task. More specifically, we study the severity of each misinformation story and how readers perceiv…
Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term
Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）；Department of Computer Science, National Tsing Hua University（国立清华大学资讯工程学系）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The Pennsylvania State University（宾夕法尼亚州立大学）
The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in…
