COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies (Preprint)

2021 ◽  
Author(s):  
Goran Muric ◽  
Yusong Wu ◽  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Herd Immunity ◽  
Application Programming Interface ◽  
Data Access ◽  
Vaccine Hesitancy ◽  
Global Public Health ◽  
News Sources ◽  
Online Social Media ◽  
Vaccination Campaigns

BACKGROUND False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, thus posing a threat to global public health. Misinformation originating from various sources has been spreading online since the beginning of the COVID-19 pandemic. Anti-vaccine activists have also begun to utilize platforms like Twitter to share their views. To properly understand the phenomenon of vaccine hesitancy through the lens of online social media, it is of greatest importance to gather the relevant data. OBJECTIVE In this paper, we describe a dataset of Twitter posts that exhibit a strong anti-vaccine stance. The dataset is made available to the research community via our AvaxTweets dataset GitHub repository. METHODS We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific anti-vaccine related keywords. Additionally, we collect the historical tweets of the set of accounts that engaged in spreading anti-vaccination narratives at some point during 2020. RESULTS Since the inception of our collection, we have published two collections: a) a streaming keyword-centered data collection with more than 1.8 million tweets, and b) a historical account-level collection with more than 135 million tweets. In this paper we present descriptive analyses showing the volume of activity over time, geographical distributions, topics, news sources, and inferred accounts’ political leaning. CONCLUSIONS The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering the progress toward vaccine-induced herd immunity, and potentially increase infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Since data access is the first obstacle to attain that, we publish the dataset that can be used in studying anti-vaccine misinformation on social media and enable a better understanding of vaccine hesitancy.

scholarly journals Are We Ready for the Arrival of the New COVID-19 Vaccinations? Great Promises and Unknown Challenges Still to Come

Vaccines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 173
Author(s):  
Davide Gori ◽  
Chiara Reno ◽  
Daniel Remondini ◽  
Francesco Durazzi ◽  
Maria Pia Fantini
Keyword(s):  
Herd Immunity ◽  
The Elderly ◽  
Vaccine Hesitancy ◽  
Current Crisis ◽  
Complex Issue ◽  
The Public ◽  
The Social ◽  
To Come ◽  
Vaccination Campaigns ◽  
The Eu

While the SARS-CoV-2 pandemic continues to strike and collect its death toll throughout the globe, as of 31 January 2021, the vaccine candidates worldwide were 292, of which 70 were in clinical testing. Several vaccines have been approved worldwide, and in particular, three have been so far authorized for use in the EU. Vaccination can be, in fact, an efficient way to mitigate the devastating effect of the pandemic and offer protection to some vulnerable strata of the population (i.e., the elderly) and reduce the social and economic burden of the current crisis. Regardless, a question is still open: after vaccination availability for the public, will vaccination campaigns be effective in reaching all the strata and a sufficient number of people in order to guarantee herd immunity? In other words: after we have it, will we be able to use it? Following the trends in vaccine hesitancy in recent years, there is a growing distrust of COVID-19 vaccinations. In addition, the online context and competition between pro- and anti-vaxxers show a trend in which anti-vaccination movements tend to capture the attention of those who are hesitant. Describing this context and analyzing its possible causes, what interventions or strategies could be effective to reduce COVID-19 vaccine hesitancy? Will social media trend analysis be helpful in trying to solve this complex issue? Are there perspectives for an efficient implementation of COVID-19 vaccination coverage as well as for all the other vaccinations?

scholarly journals Distortions of political bias in crowdsourced misinformation flagging

2020 ◽  
Vol 17 (167) ◽  
pp. 20200020
Author(s):  
Michele Coscia ◽  
Luca Rossi
Keyword(s):  
Social Media ◽  
News Item ◽  
Real World Data ◽  
Media Environment ◽  
Political Bias ◽  
News Sources ◽  
Online Social Media ◽  
News Source ◽  
Social Media Platforms ◽  
Current Content

Many people view news on social media, yet the production of news items online has come under fire because of the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced ‘flags’: users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and—supposedly—reliable ones. In this paper, we show that this phenomenon can be explained by the unreasonable assumptions that current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.

scholarly journals Fake News Detection on Social Media Using A Natural Language Inference Approach

2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi
Keyword(s):  
Social Media ◽  
Natural Language ◽  
News Item ◽  
Support Vector ◽  
Statistical Features ◽  
Fake News ◽  
K Nearest Neighbors ◽  
News Sources ◽  
Online Social Media ◽  
Embedding Methods

Abstract Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

scholarly journals Network Analysis of COVID-19 Vaccine Misinformation on Social Media

2021 ◽  
Author(s):  
Chad Melton ◽  
Olufunto A. Olusanya ◽  
Arash Shaban-Nejad
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Semantic Network ◽  
Vaccine Safety ◽  
Vaccine Hesitancy ◽  
World Population ◽  
Semantic Network Analysis ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform

Almost half of the world population has received at least one dose of vaccine against the COVID-19 virus. However, vaccine hesitancy amongst certain populations is driving new waves of infections at alarming rates. The popularity of online social media platforms attracts supporters of the anti-vaccination movement who spread misinformation about vaccine safety and effectiveness. We conducted a semantic network analysis to explore and analyze COVID-19 vaccine misinformation on the Reddit social media platform.

scholarly journals Lack of trust and social media echo chambers predict COVID-19 vaccine hesitancy

2021 ◽  
Author(s):  
Will Jennings ◽  
Gerry Stoker ◽  
Hannah Willis ◽  
Viktor Valgardsson ◽  
Jen Gaskell ◽  
...  
Keyword(s):  
Social Media ◽  
Focus Groups ◽  
Vaccine Development ◽  
Population Control ◽  
Herd Immunity ◽  
Vaccine Hesitancy ◽  
Media Impact ◽  
Media Companies ◽  
Nationally Representative ◽  
The Uk

AbstractAs COVID-19 vaccines are rolled out across the world, there are growing concerns about the role that trust, belief in conspiracy theories and spread of misinformation through social media impact vaccine hesitancy. We use a nationally representative survey of 1,476 adults in the UK between December 12 to 18, 2020 and five focus groups conducted in the same period. Trust is a core predictor, with distrust in vaccines in general and mistrust in government raising vaccine hesitancy. Trust in health institutions and experts and perceived personal threat are vital, with focus groups revealing that COVID-19 vaccine hesitancy is driven by a misunderstanding of herd immunity as providing protection, fear of rapid vaccine development and side effects, belief the virus is man- made and related to population control. Particularly those who obtain information from relatively unregulated social media sources such as YouTube that have recommendations tailored by watch history are less likely to be willing to become vaccinated. Those who hold general conspiratorial beliefs are less willing to be vaccinated. Since an increasing number of individuals use social media for gathering health information, interventions require action from governments, health officials and social media companies. More attention needs to help people understand their own risks, unpack complex concepts and fill knowledge voids.

scholarly journals Online misinformation and vaccine hesitancy

2021 ◽  
Author(s):  
Renee Garett ◽  
Sean D Young
Keyword(s):  
Social Media ◽  
Vaccine Hesitancy ◽  
Effective Vaccine ◽  
Vaccine Preventable Disease ◽  
Misleading Information ◽  
Preventable Disease ◽  
Media Companies ◽  
Online Social Media ◽  
Childhood Vaccine ◽  
Health Experts

Lay Summary Vaccine hesitancy, the rejection or delay to get vaccinated even if there is an effective vaccine available, may be instrumental in the resurgence of vaccine-preventable disease. Studies have shown that the rise in nonmedical exemptions for vaccination increases rates of childhood vaccine-preventable disease. One factor that influences vaccine hesitancy is online misinformation. False or misleading information online regarding vaccines can be found in independent news outlets, websites, and social media. The spread of vaccine misinformation is especially important during the COVID-19 pandemic as false information can decrease pro-vaccine opinions. The recent announcement of an effective COVID-19 vaccine became a hot topic online, with many adults hesitant to take the vaccine. Public health experts, medical professionals, and pro-vaccine individuals can help curb the spread of misinformation by correcting false statements online. Social media companies can also aid in stopping misinformation by implementing and enforcing policy that limits misinformation on their platforms.

scholarly journals Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

2021 ◽  
Vol 18 (24) ◽  
pp. 13028
Author(s):  
Charlotte Roe ◽  
Madison Lowe ◽  
Benjamin Williams ◽  
Clare Miller
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Public Perception ◽  
Application Programming Interface ◽  
Vaccine Hesitancy ◽  
Analysis Model ◽  
Search Terms ◽  
Machine Learning Approach ◽  
Application Programming

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

scholarly journals The shot, the message, and the messenger: COVID-19 vaccine acceptance in Latin America

npj Vaccines ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Pablo Argote ◽  
Elena Barham ◽  
Sarah Zukerman Daly ◽  
Julian E. Gerez ◽  
John Marshall ◽  
...  
Keyword(s):  
Latin America ◽  
Herd Immunity ◽  
Mass Vaccination ◽  
Vaccine Hesitancy ◽  
Vaccine Acceptance ◽  
State Institutions ◽  
Country Analysis ◽  
Foreign Governments ◽  
Vaccination Campaigns ◽  
Mass Inoculation

AbstractHerd immunity by mass vaccination offers the potential to substantially limit the continuing spread of COVID-19, but high levels of vaccine hesitancy threaten this goal. In a cross-country analysis of vaccine hesitant respondents across Latin America in January 2021, we experimentally tested how five features of mass vaccination campaigns—the vaccine’s producer, efficacy, endorser, distributor, and current population uptake rate—shifted willingness to take a COVID-19 vaccine. We find that citizens preferred Western-produced vaccines, but were highly influenced by factual information about vaccine efficacy. Vaccine hesitant individuals were more responsive to vaccine messengers with medical expertise than political, religious, or media elite endorsements. Citizen trust in foreign governments, domestic leaders, and state institutions moderated the effects of the campaign features on vaccine acceptance. These findings can help inform the design of unfolding mass inoculation campaigns.

scholarly journals Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (Preprint)

2020 ◽  
Author(s):  
Emily Chen ◽  
Kristina Lerman ◽  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Application Programming Interface ◽  
Time Data ◽  
Data Set ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Application Programming ◽  
Twitter Activity ◽  
Online Conversation

BACKGROUND At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. OBJECTIVE In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. METHODS We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. RESULTS Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. CONCLUSIONS It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

scholarly journals Lack of Trust, Conspiracy Beliefs, and Social Media Use Predict COVID-19 Vaccine Hesitancy

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 593
Author(s):  
Will Jennings ◽  
Gerry Stoker ◽  
Hannah Bunting ◽  
Viktor Orri Valgarðsson ◽  
Jennifer Gaskell ◽  
...  
Keyword(s):  
Social Media ◽  
Focus Groups ◽  
Vaccine Development ◽  
Population Control ◽  
Herd Immunity ◽  
Vaccine Hesitancy ◽  
Media Companies ◽  
Conspiracy Beliefs ◽  
Nationally Representative ◽  
The Uk

As COVID-19 vaccines are rolled out across the world, there are growing concerns about the roles that trust, belief in conspiracy theories, and spread of misinformation through social media play in impacting vaccine hesitancy. We use a nationally representative survey of 1476 adults in the UK between 12 and 18 December 2020, along with 5 focus groups conducted during the same period. Trust is a core predictor, with distrust in vaccines in general and mistrust in government raising vaccine hesitancy. Trust in health institutions and experts and perceived personal threat are vital, with focus groups revealing that COVID-19 vaccine hesitancy is driven by a misunderstanding of herd immunity as providing protection, fear of rapid vaccine development and side effects, and beliefs that the virus is man-made and used for population control. In particular, those who obtain information from relatively unregulated social media sources—such as YouTube—that have recommendations tailored by watch history, and who hold general conspiratorial beliefs, are less willing to be vaccinated. Since an increasing number of individuals use social media for gathering health information, interventions require action from governments, health officials, and social media companies. More attention needs to be devoted to helping people understand their own risks, unpacking complex concepts, and filling knowledge voids.

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