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 co...
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| Format: | Working Paper |
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World Bank, Washington, DC
2022
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| Online Access: | http://documents.worldbank.org/curated/en/099545109062215988/IDU09209a2550575104cbf0b5dc0990c0568bc5a http://hdl.handle.net/10986/37970 |
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okr-10986-379702022-09-08T05:10:28Z 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 |
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Digital Repository |
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Foreign Institution |
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Digital Repositories |
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World Bank Open Knowledge Repository |
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World Bank |
| 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 |
| 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 Van Der Weide, Roy Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the Arabic Speaking World |
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Middle East |
| relation |
Policy Research Working Papers;10165 |
| 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 |
| 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 |
| url |
http://documents.worldbank.org/curated/en/099545109062215988/IDU09209a2550575104cbf0b5dc0990c0568bc5a http://hdl.handle.net/10986/37970 |
| _version_ |
1764488227482238976 |