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. Onl...
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Washington, D.C
The World Bank
2022
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| 520 | 3 | |a 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 | |
| 650 | 4 | |a Anti-Vaccine Social Media | |
| 650 | 4 | |a Arabic Twitter | |
| 650 | 4 | |a Communicable Diseases | |
| 650 | 4 | |a Covid Vaccine Side Effect Attitudes | |
| 650 | 4 | |a COVID-19 Pandemic | |
| 650 | 4 | |a Disease Control and Prevention | |
| 650 | 4 | |a Health Behavior | |
| 650 | 4 | |a Health, Nutrition and Population | |
| 650 | 4 | |a Immunizations | |
| 650 | 4 | |a Pharmaceuticals and Pharmacoeconomics | |
| 650 | 4 | |a Positive Vaccine Messaging | |
| 650 | 4 | |a Public Health Promotion | |
| 650 | 4 | |a Public Health Survey | |
| 650 | 4 | |a Sentiment Data | |
| 650 | 4 | |a Social Media Vaccine Endorsements | |
| 650 | 4 | |a Vaccine Sentiment | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Van Der Weide, Roy |t Inferring COVID-19 Vaccine Attitudes from Twitter Data: An Application to the Arabic Speaking World |d Washington, D.C. : The World Bank, 2022 |
| 856 | 4 | 0 | |u https://doi.org/10.1596/1813-9450-10165 |x Verlag |z kostenfrei |3 Volltext |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034341679 | |
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| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1596/1813-9450-10165 |
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| indexdate | 2025-02-03T15:58:23Z |
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| spellingShingle | Van Der Weide, Roy Inferring COVID-19 Vaccine Attitudes from Twitter Data An Application to the Arabic Speaking World Anti-Vaccine Social Media Arabic Twitter Communicable Diseases Covid Vaccine Side Effect Attitudes COVID-19 Pandemic Disease Control and Prevention Health Behavior Health, Nutrition and Population Immunizations Pharmaceuticals and Pharmacoeconomics Positive Vaccine Messaging Public Health Promotion Public Health Survey Sentiment Data Social Media Vaccine Endorsements Vaccine Sentiment |
| title | Inferring COVID-19 Vaccine Attitudes from Twitter Data An Application to the Arabic Speaking World |
| title_auth | Inferring COVID-19 Vaccine Attitudes from Twitter Data An Application to the Arabic Speaking World |
| title_exact_search | 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 Roy Van Der Weide |
| title_fullStr | Inferring COVID-19 Vaccine Attitudes from Twitter Data An Application to the Arabic Speaking World Roy Van Der Weide |
| title_full_unstemmed | Inferring COVID-19 Vaccine Attitudes from Twitter Data An Application to the Arabic Speaking World Roy Van Der Weide |
| title_short | Inferring COVID-19 Vaccine Attitudes from Twitter Data |
| title_sort | inferring covid 19 vaccine attitudes from twitter data an application to the arabic speaking world |
| title_sub | An Application to the Arabic Speaking World |
| topic | Anti-Vaccine Social Media Arabic Twitter Communicable Diseases Covid Vaccine Side Effect Attitudes COVID-19 Pandemic Disease Control and Prevention Health Behavior Health, Nutrition and Population Immunizations Pharmaceuticals and Pharmacoeconomics Positive Vaccine Messaging Public Health Promotion Public Health Survey Sentiment Data Social Media Vaccine Endorsements Vaccine Sentiment |
| topic_facet | Anti-Vaccine Social Media Arabic Twitter Communicable Diseases Covid Vaccine Side Effect Attitudes COVID-19 Pandemic Disease Control and Prevention Health Behavior Health, Nutrition and Population Immunizations Pharmaceuticals and Pharmacoeconomics Positive Vaccine Messaging Public Health Promotion Public Health Survey Sentiment Data Social Media Vaccine Endorsements Vaccine Sentiment |
| url | https://doi.org/10.1596/1813-9450-10165 |
| work_keys_str_mv | AT vanderweideroy inferringcovid19vaccineattitudesfromtwitterdataanapplicationtothearabicspeakingworld |