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Linguistic Fingerprints of Pro-vaccination and Anti-vaccination Writings

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Information Management and Big Data (SIMBig 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1070))

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Abstract

Vaccination hesitancy has been gaining public attention as a global health threat. With the echo chamber effect of social media and the prevalence of misinformation, it is becoming more important to understand all aspects of anti-vaccination attitudes, especially when facts to the contrary can solidify rather than change beliefs. Starting with the question of what might drive anti- or pro-vaccination views, this paper describes a new, balanced corpus of vaccination writings. In order to gather all the linguistic signals, corpus analysis as well as feature selection and classification tasks are used to explore themes and motivations that define each class. Our results reveal that anti-vaccination writings are typically less formal. Results also indicate the possibility that the authors of such writings are processing trauma. These findings suggest that future health promotion efforts should make attempts not to talk down to individuals and should stem from a place of understanding.

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Acknowledgments

Thank you to Lauren Covey, Anna Feldman, and Susana Sotillo for teaching me and supporting this process. R. Claire Lingenfelter, thank you for the moral support and Vikki Fiore for the moral and technical support.

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Authors and Affiliations

  1. Montclair State University, Montclair, NJ, 07043, USA

    Rebecca A. Stachowicz

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  1. Rebecca A. Stachowicz
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Correspondence to Rebecca A. Stachowicz .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. University of A Coruña, A Coruña, Spain

    Nelly Condori-Fernandez

  3. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

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Stachowicz, R.A. (2020). Linguistic Fingerprints of Pro-vaccination and Anti-vaccination Writings. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_30

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  • DOI: https://doi.org/10.1007/978-3-030-46140-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46139-3

  • Online ISBN: 978-3-030-46140-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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