As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and prevent the spread of disinformation and unverified rumours. While many sophisticated and successful models have been proposed in the literature, they are often compared with older NLP baselines such as SVMs, CNNs, and LSTMs. In this paper, we examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods. We present our framework as a baseline for creating and evaluating new methods for misinformation detection. We further study a comprehensive set of benchmark datasets, and discuss potential data leakage and the need for careful design of the experiments and understanding of datasets to account for confounding variables. As an extreme case example, we show that classifying only based on the first three digits of tweet ids, which contain information on the date, gives state-of-the-art performance on a commonly used benchmark dataset for fake news detection --Twitter16. We provide a simple tool to detect this problem and suggest steps to mitigate it in future datasets.
The COVID-19 pandemic has caused globally significant impacts since the beginning of 2020. This brought a lot of confusion to society, especially due to the spread of misinformation through social media. Although there were already several studies related to the detection of misinformation in social media data, most studies focused on the English dataset. Research on COVID-19 misinformation detection in Indonesia is still scarce. Therefore, through this research, we collect and annotate datasets for Indonesian and build prediction models for detecting COVID-19 misinformation by considering the…
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…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address this issue, a common approach is to introduce a domain critic and encourage domain-invariant input features. However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e.g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation. In this paper, we propose contrastive adaptation network for early misinf…
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…
"Image, Tell me your story!" Predicting the original meta-context of visual misinformation
TU Darmstadt（达姆施塔特工业大学）；KU Leuven（鲁汶大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；Ubiquitous Knowledge Processing Lab (UKP Lab)（普适知识处理实验室）；Department of Computer Science（计算机科学系）；Department of Electrical Engineering（电气工程系）
Robust Misinformation Detection by Visiting Potential Commonsense Conflict
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of the MoE, Jilin University（吉林大学符号计算与知识工程重点实验室）；School of Computer Science and Artificial Intelligence, Liaoning Normal University（辽宁师范大学计算机科学与人工智能学院）
Why Misinformation is Created? Detecting them by Integrating Intent Features
Jilin University（吉林大学）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Liaoning Normal University（辽宁师范大学）；University of Shanghai for Science and Technology（上海理工大学）
E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection
Department of Mathematics and Statistics, American University, Washington, DC, USA（数学与统计学系，美国大学，华盛顿特区，美国）；Department of Computer Science and Mathematics, Pennsylvania State University, Harrisburg, PA, USA（计算机科学与数学系，宾夕法尼亚州立大学，哈里斯堡，宾夕法尼亚州，美国）；Department of Computer Science, American University, Washington, DC, USA（计算机科学系，美国大学，华盛顿特区，美国）
Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University（吉林大学 教育部符号计算与知识工程重点实验室）；College of Computer Science and Technology, Jilin University（吉林大学 计算机科学与技术学院）
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；College of Software, Jilin University（吉林大学软件学院）；School of Computer and Artificial Intelligence, Liaoning Normal University（辽宁师范大学计算机与人工智能学院）；School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Department of Computer Science, Swansea University（斯旺西大学计算机科学系）
Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs
Warsaw University of Technology（华沙理工大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；AGH University of Krakow（AGH科技大学）；NASK National Research Institute（NASK国家研究所）；University of Technology Sydney（悉尼科技大学）；Óbuda University（欧布达大学）；Khazar University（哈扎尔大学）
FakeZero: Real-Time, Privacy-Preserving Misinformation Detection for Facebook and X
College of Computing, Mohammed VI Polytechnic University（计算学院，摩洛哥穆莱·易卜拉欣polytechnic大学）；Cadi Ayyad University, National School of Applied Science of Safi, MISCOM Lab（卡迪·阿亚德大学，萨菲应用科学国家学校，MISCOM实验室）
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place lives of people at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled ge…
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…
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…
Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
University College London（伦敦大学学院）；University of Copenhagen（哥本哈根大学）
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose o…
Tsinghua University（清华大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Cambridge（剑桥大学）
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking. While misinformation spans both geographic and linguistic boundaries, most work in the field has focused on English. Datasets and tools available in other languages, such as Chinese, are limited. In order to bridge this gap, we construct CHEF, the first CHinese Evidence-based Fact-checking dataset of 10K real-world claims. The dataset covers multiple domains, ranging from politics to public health, and provides annotated evidence retrieved from the Internet. Further, we develop established basel…
Paul G. Allen School of CSE, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Google AI Language（谷歌AI语言部门）
In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingl…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）
In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) …
Paul G. Allen School of Computer Science and Engineering（保罗·G·艾伦计算机科学与工程学院）
We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work often compares against random or majority class baselines, we argue that unimodal approaches better capture and reflect dataset biases and therefore provide an important comparison when assessing the performance of multimodal techniques. We present unimodal ablations on three recent datasets in visual navigation and QA, seeing an up to 29% absolute gain in performance over published baselines.
