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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| Language: | English English |
| Published: |
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 |
| Summary: | 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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