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Geospatial vaccine misinformation risk on social media: Online insights from an English/Spanish natural language processing (NLP) analysis of vaccine-related tweets

Author

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  • Valdez, Danny
  • Soto-Vásquez, Arthur D.
  • Montenegro, María S.

Abstract

Misinformation is known to affect norms, attitudes, and intentions to engage with healthy behaviors. Evidence strongly supports that Spanish speakers may be particularly affected by misinformation and its outcomes, yet current insights into the scope and scale of misinformation is primarily ethnocentric, with greater emphasis on English-language design.

Suggested Citation

  • Valdez, Danny & Soto-Vásquez, Arthur D. & Montenegro, María S., 2023. "Geospatial vaccine misinformation risk on social media: Online insights from an English/Spanish natural language processing (NLP) analysis of vaccine-related tweets," Social Science & Medicine, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:socmed:v:339:y:2023:i:c:s0277953623007220
    DOI: 10.1016/j.socscimed.2023.116365
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    References listed on IDEAS

    as
    1. Danny Valdez & Andrew C. Pickett & Patricia Goodson, 2018. "Topic Modeling: Latent Semantic Analysis for the Social Sciences," Social Science Quarterly, Southwestern Social Science Association, vol. 99(5), pages 1665-1679, November.
    2. Jordi Rodríguez-Virgili & Javier Serrano-Puche & Carmen Beatriz Fernández, 2021. "Digital Disinformation and Preventive Actions: Perceptions of Users from Argentina, Chile, and Spain," Media and Communication, Cogitatio Press, vol. 9(1), pages 323-337.
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    Cited by:

    1. Kadić-Maglajlić, Selma & Lages, Cristiana R. & Pantano, Eleonora, 2024. "No time to lie: Examining the identity of pro-vaccination and anti-vaccination supporters through user-generated content," Social Science & Medicine, Elsevier, vol. 347(C).

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