Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach
- URL: http://arxiv.org/abs/2406.10238v1
- Date: Sun, 2 Jun 2024 19:27:56 GMT
- Title: Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach
- Authors: Minjia Mao, Xiaohang Zhao, Xiao Fang,
- Abstract summary: An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak.
Detecting misinformation at the early stage of an infodemic is key to manage it and reduce its harm to public health.
- Score: 1.4883782513177093
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at the early stage of an infodemic is key to manage it and reduce its harm to public health. An early stage infodemic is characterized by a large volume of unlabeled information concerning a disease. As a result, conventional misinformation detection methods are not suitable for this misinformation detection task because they rely on labeled information in the infodemic domain to train their models. To address the limitation of conventional methods, state-of-the-art methods learn their models using labeled information in other domains to detect misinformation in the infodemic domain. The efficacy of these methods depends on their ability to mitigate both covariate shift and concept shift between the infodemic domain and the domains from which they leverage labeled information. These methods focus on mitigating covariate shift but overlook concept shift, rendering them less effective for the task. In response, we theoretically show the necessity of tackling both covariate shift and concept shift as well as how to operationalize each of them. Built on the theoretical analysis, we develop a novel misinformation detection method that addresses both covariate shift and concept shift. Using two real-world datasets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over state-of-the-art misinformation detection methods as well as prevalent domain adaptation methods that can be tailored to solve the misinformation detection task.
Related papers
- Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via
Optimization Trajectory Distillation [73.83178465971552]
The success of automated medical image analysis depends on large-scale and expert-annotated training sets.
Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection.
We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
arXiv Detail & Related papers (2023-07-27T08:58:05Z) - Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised
Domain Adaptation [70.85686267987744]
Unsupervised domain adaptation problems can transfer knowledge from a labeled source domain to an unlabeled target domain.
We propose an interpretive model of unsupervised domain adaptation, as the first attempt to visually unveil the mystery of transferred knowledge.
Our method provides an intuitive explanation for the base model's predictions and unveils transfer knowledge by matching the image patches with the same semantics across both source and target domains.
arXiv Detail & Related papers (2023-03-04T03:02:12Z) - Contrastive Domain Adaptation for Early Misinformation Detection: A Case
Study on COVID-19 [8.828396559882954]
Early misinformation often demonstrates both conditional and label shifts against existing misinformation data.
We propose contrastive adaptation network for early misinformation detection (CANMD)
Results suggest CANMD can effectively adapt misinformation detection systems to the unseen COVID-19 target domain.
arXiv Detail & Related papers (2022-08-20T02:09:35Z) - Inter-Semantic Domain Adversarial in Histopathological Images [0.0]
In computer vision, data shift has proven to be a major barrier for safe and robust deep learning applications.
It is important to understand to what extent a model can be made robust against data shift using all available data.
arXiv Detail & Related papers (2022-01-22T12:55:59Z) - Descriptive vs. inferential community detection in networks: pitfalls,
myths, and half-truths [0.0]
We argue that inferential methods are more typically aligned with clearer scientific questions, yield more robust results, and should be in many cases preferred.
We attempt to dispel some myths and half-truths often believed when community detection is employed in practice, in an effort to improve both the use of such methods as well as the interpretation of their results.
arXiv Detail & Related papers (2021-11-30T23:57:51Z) - Covered Information Disentanglement: Model Transparency via Unbiased
Permutation Importance [2.064612766965483]
We show how to compute Covered Information Disentanglement (CID) efficiently when coupled with Markov random fields.
We demonstrate its efficacy in adjusting permutation importance first on a controlled toy dataset and discuss its effect on real-world medical data.
arXiv Detail & Related papers (2021-11-18T15:13:28Z) - Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or
Something Else? [93.91375268580806]
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms.
Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications.
By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
arXiv Detail & Related papers (2021-11-09T13:30:34Z) - Deep Transfer Learning for Infectious Disease Case Detection Using
Electronic Medical Records [0.0]
During an infectious disease pandemic, it is critical to share electronic medical records or models (learned from these records) across regions.
Applying one region's data/model to another region often have distribution shift issues that violate the assumptions of traditional machine learning techniques.
To explore the potential of deep transfer learning algorithms, we applied two data-based algorithms and model-based transfer learning algorithms to infectious disease detection tasks.
arXiv Detail & Related papers (2021-03-08T01:53:29Z) - A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks.
In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data.
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
arXiv Detail & Related papers (2020-09-01T00:06:50Z) - Explainable Deep Classification Models for Domain Generalization [94.43131722655617]
Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision.
Our training strategy enforces a periodic saliency-based feedback to encourage the model to focus on the image regions that directly correspond to the ground-truth object.
arXiv Detail & Related papers (2020-03-13T22:22:15Z) - Understanding Self-Training for Gradual Domain Adaptation [107.37869221297687]
We consider gradual domain adaptation, where the goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain.
We prove the first non-vacuous upper bound on the error of self-training with gradual shifts, under settings where directly adapting to the target domain can result in unbounded error.
The theoretical analysis leads to algorithmic insights, highlighting that regularization and label sharpening are essential even when we have infinite data, and suggesting that self-training works particularly well for shifts with small Wasserstein-infinity distance.
arXiv Detail & Related papers (2020-02-26T08:59:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.