Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse

• URL: http://arxiv.org/abs/2202.09445v1
• Date: Fri, 18 Feb 2022 22:01:49 GMT
• Title: Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse
• Authors: Maxwell Weinzierl, Sanda Harabagiu
• Abstract summary: Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. We describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. Experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel neural architecture, where the stance towards misinformation is organized in a knowledge graph. This new neural framework is enabling the identification of stance towards misinformation about COVID-19 vaccines with state-of-the-art results. The experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse. Because CoVaxLies provides a taxonomy of the misinformation about COVID-19 vaccines, we are able to show which type of misinformation is mostly adopted and which is mostly rejected.

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)
• 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)
• VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter [6.061534265076204]
  We create a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance) To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.
  arXiv  Detail & Related papers  (2023-01-17T02:00:31Z)
• Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification [60.49594822215981]
  This paper presents a classification model for detecting COVID-19 vaccination related search queries. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
  arXiv  Detail & Related papers  (2022-12-16T13:57:41Z)
• Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers [64.1526243118151]
  We find that doctors are overall more positive toward the COVID-19 vaccines. Doctors are more concerned with the effectiveness of the vaccines over newer variants. Nurses pay more attention to the potential side effects on children.
  arXiv  Detail & Related papers  (2022-09-11T14:22:16Z)
• "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
  We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
  arXiv  Detail & Related papers  (2022-06-12T19:41:01Z)
• VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines [0.0]
  VaccineLies is a collection of tweets propagating misinformation about two vaccines: the COVID-19 vaccines and the Human Papillomavirus (HPV) vaccines. The ability to automatically recognize misinformation targeting vaccines on Twitter depends on the availability of data resources.
  arXiv  Detail & Related papers  (2022-02-18T22:09:38Z)
• Cross-lingual COVID-19 Fake News Detection [54.125563009333995]
  We make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English) We propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content. Empirical results on our dataset demonstrate the effectiveness of CrossFake under the cross-lingual setting.
  arXiv  Detail & Related papers  (2021-10-13T04:44:02Z)
• 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)
• The illicit trade of COVID-19 vaccines on the dark web [55.45786602961871]
  Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started. Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available. We show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020.
  arXiv  Detail & Related papers  (2021-02-10T14:52:54Z)
• 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)

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.