Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this paper, we consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features in order to reduce the effects of the confounding variables. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features. We investigate multiple types of fake news methods (classifiers) such as logistic regression, random forests, and support vector machines. We have consistent observations of performance improvement.
The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an `infodemic' by the CDC and WHO. Misinformation tied to the Covid-19 infodemic changes continuously; this can lead to performance degradation of fine-tuned models due to concept drift. Degredation can be mitigated if models generalize well-enough to capture some cyclical aspects of drifted data. In this paper, we explore generalizability of pre-trained and fine-tuned fake news detectors across 9 fake news datasets. We show that existing models often overfit on their training dataset and have po…
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and other related components (such as news creators and news topics) can be modeled as a heterogeneous information network (HIN for short). In this paper, we propose a novel fake news detection framework, n…
The Consent-to-Contact (C2C) registry at the University of California, Irvine collects data from community participants to aid in the recruitment to clinical research studies. Self-selection into the C2C likely leads to bias due in part to enrollees having more years of education relative to the US general population. Salazar et al. (2020) recently used the C2C to examine associations of race/ethnicity with participant willingness to be contacted about research studies. To address questions about generalizability of estimated associations we estimate propensity for self-selection into the conv…
LingML: Linguistic-Informed Machine Learning for Enhanced Fake News Detection
Nanyang Technological University (NTU)（南洋理工大学）；University College London (UCL)（伦敦大学学院）；Singapore University of Social Sciences（新加坡社会科学大学）；Singapore Institute of Technology（新加坡科技设计大学）
In contrast to conventional, univariate analysis, various types of multivariate analysis have been applied to functional magnetic resonance imaging (fMRI) data. In this paper, we compare two contemporary approaches for multivariate regression on task-based fMRI data: linear regression with ridge regularization and non-linear symbolic regression using genetic programming. The data for this project is representative of a contemporary fMRI experimental design for visual stimuli. Linear and non-linear models were generated for 10 subjects, with another 4 withheld for validation. Model quality is e…
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
Department of Computer Science, Faculty of Electrical Engineering and Computer science, VSB-Technical University of Ostrava（计算机科学系，电气工程与计算机科学学院，VSB技术大学奥斯特拉瓦）；VSB-Technical University of Ostrava（VSB技术大学奥斯特拉瓦）；IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava（IT4Innovations国家超级计算中心，VSB技术大学奥斯特拉瓦）；Department of Applied Mathematics, Faculty of Electrical Engineering and Computer science, VSB-Technical University of Ostrava（应用数学系，电气工程与计算机科学学院，VSB技术大学奥斯特拉瓦）；Department of Surgical Studies, Faculty of Medicine, University of Ostrava（外科研究系，医学院，奥斯特拉瓦大学）；Department of Surgery, University Hospital Ostrava（外科系，奥斯特拉瓦大学医院）
Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Homelessness, Behavioral Health Problems, and Recidivism
Hong Kong University of Science and Technology（香港科技大学）；Facebook AI；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and model's general…
The spread of fake news has long been a social issue and the necessity of identifying it has become evident since its dangers are well recognized. In addition to causing uneasiness among the public, it has even more devastating consequences. For instance, it might lead to death during pandemics due to unverified medical instructions. This study aims to build a model for identifying fake news using graphs and machine learning algorithms. Instead of scanning the news content or user information, the research explicitly focuses on the spreading network, which shows the interconnection among peopl…
Florida State University（佛罗里达州立大学）；Jilin University（吉林大学）
The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relationships can be modeled as a heterogeneous information network (HIN). In this paper, we attempt to solve the fake news detection problem with the support of a news-oriented HIN. We propose a novel fak…
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）
