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

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  • Van Der Weide,Roy

Abstract

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

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  • Van Der Weide,Roy, 2022. "Inferring COVID-19 Vaccine Attitudes from Twitter Data : An Application to the ArabicSpeaking World," Policy Research Working Paper Series 10165, The World Bank.
    • Handle: RePEc:wbk:wbrwps:10165
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    1. Vivi Alatas & Arun G. Chandrasekhar & Markus Mobius & Benjamin A. Olken & Cindy Paladines, 2019. "When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination In Indonesia," NBER Working Papers 25589, National Bureau of Economic Research, Inc.
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