CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research

• URL: http://arxiv.org/abs/2207.01505v1
• Date: Thu, 30 Jun 2022 05:58:35 GMT
• Title: CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research
• Authors: Bohan Jiang, Paras Sheth, Baoxin Li, Huan Liu
• Abstract summary: A substantial proportion of the population is still hesitant to be vaccinated against the COVID-19 virus. Existing datasets fail to cover all these aspects, making it difficult to form a complete picture in inferencing about the problem of vaccine hesitancy. In this paper, we construct a multi-source, multi-modal, and multi-feature online-offline data repository CoVaxNet.
• Score: 39.82073461647643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the astonishing success of COVID-19 vaccines against the virus, a substantial proportion of the population is still hesitant to be vaccinated, undermining governmental efforts to control the virus. To address this problem, we need to understand the different factors giving rise to such a behavior, including social media discourses, news media propaganda, government responses, demographic and socioeconomic statuses, and COVID-19 statistics, etc. However, existing datasets fail to cover all these aspects, making it difficult to form a complete picture in inferencing about the problem of vaccine hesitancy. In this paper, we construct a multi-source, multi-modal, and multi-feature online-offline data repository CoVaxNet. We provide descriptive analyses and insights to illustrate critical patterns in CoVaxNet. Moreover, we propose a novel approach for connecting online and offline data so as to facilitate the inference tasks that exploit complementary information sources.

Related papers

• Hierarchical Multi-Label Classification of Online Vaccine Concerns [8.271202196208]
  Vaccine concerns are an ever-evolving target, and can shift quickly as seen during the COVID-19 pandemic. We explore the task of detecting vaccine concerns in online discourse using large language models (LLMs) in a zero-shot setting without the need for expensive training datasets.
  arXiv  Detail & Related papers  (2024-02-01T20:56:07Z)
• Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter [3.768191396638854]
  Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. We present Vax-Culture, a novel Twitter COVID-19 dataset consisting of 6373 vaccine-related tweets. We hope this can lead to effective and targeted public health communication strategies for reaching individuals with anti-vaccine beliefs.
  arXiv  Detail & Related papers  (2023-04-13T23:04:30Z)
• Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response [21.13458519284399]
  Many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state.
  arXiv  Detail & Related papers  (2022-02-09T19:57:55Z)
• Insta-VAX: A Multimodal Benchmark for Anti-Vaccine and Misinformation Posts Detection on Social Media [32.252687203366605]
  Anti-vaccine posts on social media have been shown to create confusion and reduce the publics confidence in vaccines. Insta-VAX is a new multi-modal dataset consisting of a sample of 64,957 Instagram posts related to human vaccines.
  arXiv  Detail & Related papers  (2021-12-15T20:34:57Z)
• Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction [2.0625936401496237]
  Vaccine hesitancy fueled by social media misinformation about COVID-19 vaccines became a major hurdle. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem.
  arXiv  Detail & Related papers  (2021-08-04T23:27:10Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• 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)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)
• COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images [93.0013343535411]
  We introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases.
  arXiv  Detail & Related papers  (2020-03-22T12:26:36Z)

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.