Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation

• URL: http://arxiv.org/abs/2010.08743v2
• Date: Mon, 25 Apr 2022 17:37:48 GMT
• Title: Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation
• Authors: Arkin Dharawat and Ismini Lourentzou and Alex Morales and ChengXiang Zhai
• Abstract summary: We frame health misinformation as a risk assessment task. We study the severity of each misinformation story and how readers perceive this severity. We evaluate several traditional and state-of-the-art models and show there is a significant gap in performance.
• Score: 23.449057978351945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 perceive this severity, i.e., how harmful a message believed by the audience can be and what type of signals can be used to recognize potentially malicious fake news and detect refuted claims. To address our research questions, we introduce a new benchmark dataset, accompanied by detailed data analysis. We evaluate several traditional and state-of-the-art models and show there is a significant gap in performance when applying traditional misinformation classification models to this task. We conclude with open challenges and future directions.

Related papers

• AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System [0.05461938536945722]
  This work explored how existing information obtained from social media can be harnessed to facilitate automated rebuttal of misinformation at scale. It leverages two publicly available datasets, FaCov (fact-checked articles) and misleading (social media Twitter) data on COVID-19 Vaccination.
  arXiv  Detail & Related papers  (2023-10-29T13:07:33Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic [6.137022734902771]
  We introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation. The dataset not only allows social behaviours analysis but also suitable for both evidence-based or non-evidence-based misinformation classification task.
  arXiv  Detail & Related papers  (2021-06-22T12:17:53Z)
• Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media [7.194177427819438]
  This paper presents a mechanism to detect COVID-19 health-related misinformation in social media. We defined misinformation themes and associated keywords incorporated into the misinformation detection mechanism using applied machine learning techniques. Our method shows promising results with at most 78% accuracy in classifying health-related misinformation versus true information.
  arXiv  Detail & Related papers  (2021-06-12T16:26:04Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges [35.0667998623823]
  A large fraction of the common public turn to social media platforms for news and even information regarding highly concerning issues such as COVID-19 symptoms. There is a significant amount of ongoing research in the directions of disinformation detection and mitigation. We discuss the online disinformation problem, focusing on the recent 'infodemic' in the wake of the coronavirus pandemic.
  arXiv  Detail & Related papers  (2020-10-18T21:44:23Z)
• Towards Domain-Specific Characterization of Misinformation [14.136862418249764]
  The rapid dissemination of health misinformation poses an increasing risk to public health. It is important to acknowledge how the fundamental characteristics of misinformation differ from domain to domain. This paper presents a pathway towards domain-specific characterization of misinformation.
  arXiv  Detail & Related papers  (2020-07-29T12:46:45Z)
• Misinformation Has High Perplexity [55.47422012881148]
  We propose to leverage the perplexity to debunk false claims in an unsupervised manner. First, we extract reliable evidence from scientific and news sources according to sentence similarity to the claims. Second, we prime a language model with the extracted evidence and finally evaluate the correctness of given claims based on the perplexity scores at debunking time.
  arXiv  Detail & Related papers  (2020-06-08T15:13:44Z)
• Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals [53.484562601127195]
  We point out the inability to infer behavioral conclusions from probing results. We offer an alternative method that focuses on how the information is being used, rather than on what information is encoded.
  arXiv  Detail & Related papers  (2020-06-01T15:00:11Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)

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.