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Une démonstration de la pondération par score de propension pour ajuster un sondage non probabiliste d'un échantillon de médias sociaux sur les attitudes politiques
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We investigate whether propensity score weighting can balance differences between probability and nonprobability samples of Twitter users to evaluate the feasibility of using social media data for producing generalizable inferences on public opinion
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Propensity score weighting may help bridge the gap between social media and probability samples, but its effectiveness is still unproven.
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Utilizing social media data can enhance our understanding of public opinion by providing real-time insights that traditional methods may miss.
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Social media platforms like Twitter reflect a diverse range of political attitudes, making them valuable for gauging public sentiment across demographics.
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Relying on nonprobability samples from social media can lead to biased conclusions, undermining the validity of public opinion research.
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The reliance on social media for public opinion should be approached with caution, as it may not accurately represent the broader population's views.
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