IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0216625.html
   My bibliography  Save this article

Using selfies to challenge public stereotypes of scientists

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216625
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0216625&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0216625?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jabłońska-Sabuka, Matylda & Sitarz, Robert & Kraslawski, Andrzej, 2014. "Forecasting research trends using population dynamics model with Burgers’ type interaction," Journal of Informetrics, Elsevier, vol. 8(1), pages 111-122.
    2. Ali Daud & Muhammad Ahmad & M. S. I. Malik & Dunren Che, 2015. "Using machine learning techniques for rising star prediction in co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1687-1711, February.
    3. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    4. Sharad Goel & Ashton Anderson & Jake Hofman & Duncan J. Watts, 2016. "The Structural Virality of Online Diffusion," Management Science, INFORMS, vol. 62(1), pages 180-196, January.
    5. Cui, Hao & Kertész, János, 2023. "“Born in Rome” or “Sleeping Beauty”: Emergence of hashtag popularity on the Chinese microblog Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    6. Jaebong Son & Jintae Lee & Kai R. Larsen & Jiyoung Woo, 2020. "Understanding the uncertainty of disaster tweets and its effect on retweeting: The perspectives of uncertainty reduction theory and information entropy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1145-1161, October.
    7. Wai Hong Tan & Feng Chen, 2021. "Predicting the popularity of tweets using internal and external knowledge: an empirical Bayes type approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 335-352, June.
    8. Arora, Anuja & Bansal, Shivam & Kandpal, Chandrashekhar & Aswani, Reema & Dwivedi, Yogesh, 2019. "Measuring social media influencer index- insights from facebook, Twitter and Instagram," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 86-101.
    9. Son, Jaebong & Lee, Hyung Koo & Jin, Sung & Lee, Jintae, 2019. "Content features of tweets for effective communication during disasters: A media synchronicity theory perspective," International Journal of Information Management, Elsevier, vol. 45(C), pages 56-68.
    10. António Fonseca & Jorge Louçã, 2018. "Explaining the emergence of online popularity through a model of information diffusion," Computational and Mathematical Organization Theory, Springer, vol. 24(2), pages 169-187, June.
    11. Chise Diana & Fort Margherita & Monfardini Chiara, 2021. "On the Intergenerational Transmission of STEM Education among Graduate Students," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 21(1), pages 115-145, January.
    12. Pia M. Orrenius & Madeline Zavodny, 2015. "Does Immigration Affect Whether US Natives Major in Science and Engineering?," Journal of Labor Economics, University of Chicago Press, vol. 33(S1), pages 79-108.
    13. Jamin D. Speer, 2020. "Where the girls are: Examining and explaining the gender gap in the nursing major," Scottish Journal of Political Economy, Scottish Economic Society, vol. 67(3), pages 322-343, July.
    14. Shulamit Kahn & Donna Ginther, 2017. "Women and STEM," NBER Working Papers 23525, National Bureau of Economic Research, Inc.
    15. Oliver, Daniel & Fairlie, Robert & Millhauser, Glenn & Roland, Randa, 2021. "Minority student and teaching assistant interactions in STEM," Economics of Education Review, Elsevier, vol. 83(C).
    16. Hayter, Christopher S. & Parker, Marla A., 2019. "Factors that influence the transition of university postdocs to non-academic scientific careers: An exploratory study," Research Policy, Elsevier, vol. 48(3), pages 556-570.
    17. Biewen, Martin & Schwerter, Jakob, 2019. "Does More Math in High School Increase the Share of Female STEM Workers? Evidence from a Curriculum Reform," IZA Discussion Papers 12236, Institute of Labor Economics (IZA).
    18. Bratti, Massimiliano & Lippo, Enrico, 2022. "COVID-19 and the Gender Gap in University Student Performance," IZA Discussion Papers 15456, Institute of Labor Economics (IZA).
    19. Canaan, Serena & Mouganie, Pierre, 2019. "Female Science Advisors and the STEM Gender Gap," IZA Discussion Papers 12415, Institute of Labor Economics (IZA).
    20. Diana Chise & Margherita Fort & Chiara Monfardini, 2020. "Scientifico! like Dad: On the Intergenerational Transmission of STEM Education," FBK-IRVAPP Working Papers 2020-01, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0216625. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.