Automated clustering of COVID-19 anti-vaccine discourse on Twitter

• URL: http://arxiv.org/abs/2203.01549v1
• Date: Thu, 3 Mar 2022 07:38:31 GMT
• Title: Automated clustering of COVID-19 anti-vaccine discourse on Twitter
• Authors: Ignacio Ojea Quintana and Marc Cheong and Mark Alfano and Ritsaart Reimann and Colin Klein
• Abstract summary: An observational study of Twitter vaccine discourse is found in Ojea Quintana et al. This work expands upon Ojea Quintana et al. (2021) with two main contributions from data science. First, based on the authors' initial network clustering and qualitative analysis techniques, we are able to clearly demarcate and visualize the language patterns used in discourse by Antivaxxers. Second, using the characteristics of Antivaxxers' tweets, we develop text classifiers to determine the likelihood a given user is employing anti-vaccination language.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attitudes about vaccination have become more polarized; it is common to see vaccine disinformation and fringe conspiracy theories online. An observational study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the authors analyzed approximately six months' of Twitter discourse -- 1.3 million original tweets and 18 million retweets between December 2019 and June 2020, ranging from before to after the establishment of Covid-19 as a pandemic. This work expands upon Ojea Quintana et al. (2021) with two main contributions from data science. First, based on the authors' initial network clustering and qualitative analysis techniques, we are able to clearly demarcate and visualize the language patterns used in discourse by Antivaxxers (anti-vaccination campaigners and vaccine deniers) versus other clusters (collectively, Others). Second, using the characteristics of Antivaxxers' tweets, we develop text classifiers to determine the likelihood a given user is employing anti-vaccination language, ultimately contributing to an early-warning mechanism to improve the health of our epistemic environment and bolster (and not hinder) public health initiatives.

Related papers

• 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)
• Vaccine Discourse on Twitter During the COVID-19 Pandemic [0.7161783472741748]
  This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected. We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs.
  arXiv  Detail & Related papers  (2022-07-23T13:50:51Z)
• "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)
• Psycho-linguistic differences among competing vaccination communities on social media [0.0]
  During the COVID-19 pandemic, anti-vaxxers use social media to distribute fake news and anxiety-provoking information about the vaccine. Here, we characterize the psycho-linguistic features of anti-vaxxers on the online social network Twitter.
  arXiv  Detail & Related papers  (2021-11-09T16:18:38Z)
• COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter [9.834635805575582]
  Public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. This study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets.
  arXiv  Detail & Related papers  (2021-07-29T17:31:11Z)
• COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies [10.505633521103018]
  False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns. We present a dataset of Twitter posts that exhibit a strong anti-vaccine stance.
  arXiv  Detail & Related papers  (2021-05-11T15:43:41Z)
• Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings [0.8808021343665321]
  We extracted a corpus of Twitter posts related to COVID-19 vaccination. We created two classes of lexical categories - Emotions and Influencing factors. Negative emotions like hesitancy towards vaccines have a high correlation with health-related effects and misinformation.
  arXiv  Detail & Related papers  (2021-04-02T16:13:16Z)
• 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)
• Dark Web Marketplaces and COVID-19: before the vaccine [53.447910186085586]
  We analyse 851,199 listings extracted from 30 dark web marketplaces between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs.
  arXiv  Detail & Related papers  (2020-08-04T14:27:41Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
  We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers" At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
  arXiv  Detail & Related papers  (2020-03-26T13:55:50Z)

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.