Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World

This study investigates whether Twitter data can be used to infer attitudes towards COVID-19 vaccination with an application to the Arabic speaking world. At first glance, anti-vaccine sentiment estimated from Twitter data is surprisingly low in comparison to estimates obtained from survey data. Only about 3 percent of Twitter accounts in our database are identified as anti-COVID-vaccination (compared to 20 to 30 percent of survey respondents). This bias is resolved when: (1) filtering out accounts belonging to organizations that make up a significant share of the discourse on Twitter, and (2) adjusting for the fact that the population of Twitter users is biased towards more educated individuals. The most effective messages on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects. In the pro-vaccine camp, tweets containing content showing public figures receiving the vaccine are found to have the largest reach by far.

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Bibliographic Details
Main Author: Van Der Weide, Roy
Format: Working Paper biblioteca
Language:English
English
Published: World Bank, Washington, DC 2022-09
Subjects:VACCINE SENTIMENT, ARABIC TWITTER SENTIMENT DATA, ANTI-VACCINE SOCIAL MEDIA, COVID VACCINE SIDE EFFECT ATTITUDES, SOCIAL MEDIA VACCINE ENDORSEMENTS, POSITIVE VACCINE MESSAGING, COVID-19 PANDEMIC, HEALTH BEHAVIOR, PUBLIC HEALTH PROMOTION, PUBLIC HEALTH SURVEY VS TWITTER DATA,
Online Access:http://documents.worldbank.org/curated/en/099545109062215988/IDU09209a2550575104cbf0b5dc0990c0568bc5a
http://hdl.handle.net/10986/37970
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spelling dig-okr-10986379702022-09-08T05:10:28Z Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World Van Der Weide, Roy VACCINE SENTIMENT ARABIC TWITTER SENTIMENT DATA ANTI-VACCINE SOCIAL MEDIA COVID VACCINE SIDE EFFECT ATTITUDES SOCIAL MEDIA VACCINE ENDORSEMENTS POSITIVE VACCINE MESSAGING COVID-19 PANDEMIC HEALTH BEHAVIOR PUBLIC HEALTH PROMOTION PUBLIC HEALTH SURVEY VS TWITTER DATA This study investigates whether Twitter data can be used to infer attitudes towards COVID-19 vaccination with an application to the Arabic speaking world. At first glance, anti-vaccine sentiment estimated from Twitter data is surprisingly low in comparison to estimates obtained from survey data. Only about 3 percent of Twitter accounts in our database are identified as anti-COVID-vaccination (compared to 20 to 30 percent of survey respondents). This bias is resolved when: (1) filtering out accounts belonging to organizations that make up a significant share of the discourse on Twitter, and (2) adjusting for the fact that the population of Twitter users is biased towards more educated individuals. The most effective messages on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects. In the pro-vaccine camp, tweets containing content showing public figures receiving the vaccine are found to have the largest reach by far. 2022-09-07T15:49:17Z 2022-09-07T15:49:17Z 2022-09 Working Paper http://documents.worldbank.org/curated/en/099545109062215988/IDU09209a2550575104cbf0b5dc0990c0568bc5a http://hdl.handle.net/10986/37970 English en Policy Research Working Papers;10165 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Policy Research Working Paper Publications & Research Middle East
institution Banco Mundial
collection DSpace
country Estados Unidos
countrycode US
component Bibliográfico
access En linea
databasecode dig-okr
tag biblioteca
region America del Norte
libraryname Biblioteca del Banco Mundial
language English
English
topic VACCINE SENTIMENT
ARABIC TWITTER SENTIMENT DATA
ANTI-VACCINE SOCIAL MEDIA
COVID VACCINE SIDE EFFECT ATTITUDES
SOCIAL MEDIA VACCINE ENDORSEMENTS
POSITIVE VACCINE MESSAGING
COVID-19 PANDEMIC
HEALTH BEHAVIOR
PUBLIC HEALTH PROMOTION
PUBLIC HEALTH SURVEY VS TWITTER DATA
VACCINE SENTIMENT
ARABIC TWITTER SENTIMENT DATA
ANTI-VACCINE SOCIAL MEDIA
COVID VACCINE SIDE EFFECT ATTITUDES
SOCIAL MEDIA VACCINE ENDORSEMENTS
POSITIVE VACCINE MESSAGING
COVID-19 PANDEMIC
HEALTH BEHAVIOR
PUBLIC HEALTH PROMOTION
PUBLIC HEALTH SURVEY VS TWITTER DATA
spellingShingle VACCINE SENTIMENT
ARABIC TWITTER SENTIMENT DATA
ANTI-VACCINE SOCIAL MEDIA
COVID VACCINE SIDE EFFECT ATTITUDES
SOCIAL MEDIA VACCINE ENDORSEMENTS
POSITIVE VACCINE MESSAGING
COVID-19 PANDEMIC
HEALTH BEHAVIOR
PUBLIC HEALTH PROMOTION
PUBLIC HEALTH SURVEY VS TWITTER DATA
VACCINE SENTIMENT
ARABIC TWITTER SENTIMENT DATA
ANTI-VACCINE SOCIAL MEDIA
COVID VACCINE SIDE EFFECT ATTITUDES
SOCIAL MEDIA VACCINE ENDORSEMENTS
POSITIVE VACCINE MESSAGING
COVID-19 PANDEMIC
HEALTH BEHAVIOR
PUBLIC HEALTH PROMOTION
PUBLIC HEALTH SURVEY VS TWITTER DATA
Van Der Weide, Roy
Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World
description This study investigates whether Twitter data can be used to infer attitudes towards COVID-19 vaccination with an application to the Arabic speaking world. At first glance, anti-vaccine sentiment estimated from Twitter data is surprisingly low in comparison to estimates obtained from survey data. Only about 3 percent of Twitter accounts in our database are identified as anti-COVID-vaccination (compared to 20 to 30 percent of survey respondents). This bias is resolved when: (1) filtering out accounts belonging to organizations that make up a significant share of the discourse on Twitter, and (2) adjusting for the fact that the population of Twitter users is biased towards more educated individuals. The most effective messages on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects. In the pro-vaccine camp, tweets containing content showing public figures receiving the vaccine are found to have the largest reach by far.
format Working Paper
topic_facet VACCINE SENTIMENT
ARABIC TWITTER SENTIMENT DATA
ANTI-VACCINE SOCIAL MEDIA
COVID VACCINE SIDE EFFECT ATTITUDES
SOCIAL MEDIA VACCINE ENDORSEMENTS
POSITIVE VACCINE MESSAGING
COVID-19 PANDEMIC
HEALTH BEHAVIOR
PUBLIC HEALTH PROMOTION
PUBLIC HEALTH SURVEY VS TWITTER DATA
author Van Der Weide, Roy
author_facet Van Der Weide, Roy
author_sort Van Der Weide, Roy
title Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World
title_short Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World
title_full Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World
title_fullStr Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World
title_full_unstemmed Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World
title_sort inferring covid-19 vaccine attitudes from twitter data : an application to the arabic speaking world
publisher World Bank, Washington, DC
publishDate 2022-09
url http://documents.worldbank.org/curated/en/099545109062215988/IDU09209a2550575104cbf0b5dc0990c0568bc5a
http://hdl.handle.net/10986/37970
work_keys_str_mv AT vanderweideroy inferringcovid19vaccineattitudesfromtwitterdataanapplicationtothearabicspeakingworld
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