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Using selfies to challenge public stereotypes of scientists

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  • Paige Brown Jarreau
  • Imogene A Cancellare
  • Becky J Carmichael
  • Lance Porter
  • Daniel Toker
  • Samantha Z Yammine

Abstract

In an online Qualtrics panel survey experiment (N = 1620), we found that scientists posting self-portraits (“selfies”) to Instagram from the science lab/field were perceived as significantly warmer and more trustworthy, and no less competent, than scientists posting photos of only their work. Participants who viewed scientist selfies, especially posts containing the face of a female scientist, perceived scientists as significantly warmer than did participants who saw science-only images or control images. Participants who viewed selfies also perceived less symbolic threat from scientists. Most encouragingly, participants viewing selfies, either of male or female scientists, did not perceive scientists as any less competent than did participants viewing science-only or control images. Subjects who viewed female scientist selfies also perceived science as less exclusively male. Our findings suggest that self-portraiture by STEM professionals on social media can mitigate negative attitudes toward scientists.

Suggested Citation

  • Paige Brown Jarreau & Imogene A Cancellare & Becky J Carmichael & Lance Porter & Daniel Toker & Samantha Z Yammine, 2019. "Using selfies to challenge public stereotypes of scientists," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0216625
    DOI: 10.1371/journal.pone.0216625
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    1. Zongyang Ma & Aixin Sun & Gao Cong, 2013. "On predicting the popularity of newly emerging hashtags in Twitter," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(7), pages 1399-1410, July.
    2. Zongyang Ma & Aixin Sun & Gao Cong, 2013. "On predicting the popularity of newly emerging hashtags in Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(7), pages 1399-1410, July.
    3. Griffith, Amanda L., 2010. "Persistence of women and minorities in STEM field majors: Is it the school that matters?," Economics of Education Review, Elsevier, vol. 29(6), pages 911-922, December.
    4. Andrea Paulus & Michaela Rohr & Ron Dotsch & Dirk Wentura, 2016. "Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-18, March.
    5. Anthony Dudo & John C Besley, 2016. "Scientists’ Prioritization of Communication Objectives for Public Engagement," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
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    Cited by:

    1. Anne Reif & Tim Kneisel & Markus Schäfer & Monika Taddicken, 2020. "Why Are Scientific Experts Perceived as Trustworthy? Emotional Assessment within TV and YouTube Videos," Media and Communication, Cogitatio Press, vol. 8(1), pages 191-205.

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