2004
Volume 4, Issue 1
  • ISSN: 2665-9085
  • E-ISSN: 2665-9085

Abstract

Abstract

We examine the impact of candidates’ gender on the body language that they employ in their political advertisements. Using data on over 1,600 candidates appearing in almost 5,400 political ads that aired in the U.S. between 2017 and 2020, we employ automatic pose detection to trace the movement of their hands. We find, consistent with gender stereotypes, that male candidates use more assertive hand movements than female candidates. We also find evidence of more assertiveness among Democratic candidates and among candidates running for U.S. House, U.S. Senate, and governor.

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2022-02-01
2022-10-07
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References

  1. Bauer, N. M. (2015). Emotional, sensitive, and unfit for office? Gender stereotype activation and support female candidates. Political Psychology, 36(6), 691-708.
    [Google Scholar]
  2. Berry, W. D., Berkman, M. B., & Schneiderman, S. (2000). Legislative professionalism and incumbent reelection: the development of institutional boundaries. The American Political Science Review, 94 (4), 859–874.
    [Google Scholar]
  3. Boussalis, C., Coan, T. G., Holman, M. R., & Müller, S. (2021). Gender, candidate emotional expression, and voter reactions during televised debates. American Political Science Review, 115(4), 1242-1257. http://doi:10.1017/S0003055421000666
    [Google Scholar]
  4. Brooks, D. J. (2011). Testing the double standard for candidate emotionality: Voter reactions to the tears and anger of male and female politicians. The Journal of Politics, 73(2), 597–615.
    [Google Scholar]
  5. Brooks, D. J. (2013). He runs, she runs. Princeton University Press.
    [Google Scholar]
  6. Bucy, E. P., & Grabe, M. E. (2007). Taking television seriously: A sound and image bite analysis of presidential campaign coverage, 1992–2004. Journal of Communication, 57(4), 652–675.
    [Google Scholar]
  7. Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018). Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) (pp. 67–74).
    [Google Scholar]
  8. Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., & Sheikh, Y. A. (2019). Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence.
    [Google Scholar]
  9. Carpinella, C., & Bauer, N. M. (2021). A visual analysis of gender stereotypes in campaign advertising. Politics, Groups, and Identities, 9(2), 369–386.
    [Google Scholar]
  10. Carsey, T. M., Winburn, J., & Berry, W. D. (2017). Rethinking the normal vote, the personal vote, and the impact of legislative professionalism in U.S. state legislative elections. State Politics and Policy Quarterly, 17(4), 465–488.
    [Google Scholar]
  11. Cassese, E. C., & Holman, M. R. (2018). Party and gender stereotypes in campaign attacks. Political Behavior, 40(3), 785–807.
    [Google Scholar]
  12. Dolan, K. (2018). Voting for women: How the public evaluates women candidates. Routledge.
    [Google Scholar]
  13. Eagly, A. H., & Karau, S. J. (2002). Role congruity theory of prejudice toward female leaders. Psychological Review, 109(3), 573.
    [Google Scholar]
  14. Esler, T. (2019). Face recognition using pytorch. Retrieved from https://github.com/timesler/facenet-pytorch
    [Google Scholar]
  15. Everitt, J., Best, L. A., & Gaudet, D. (2016). Candidate gender, behavioral style, and willingness to vote: support for female candidates depends on conformity to gender norms. American Behavioral Scientist, 60(14), 1737–1755.
    [Google Scholar]
  16. Fang, H.-S., Xie, S., Tai, Y.-W., & Lu, C. (2017). RMPE: Regional multi-person pose estimation. In Iccv.
    [Google Scholar]
  17. Hayes, D. (2005). Candidate qualities through a partisan lens: A theory of trait ownership. American Journal of Political Science, 49(4), 908–923.
    [Google Scholar]
  18. Herrnson, P. S., Lay, J. C., & Stokes, A. K. (2003). Women running “as women”: Candidate gender, campaign issues, and voter-targeting strategies. Journal of Politics, 65(1), 244–255.
    [Google Scholar]
  19. Holman, M. R., Schneider, M. C., & Pondel, K. (2015). Gender targeting in political advertisements. Political Research Quarterly, 68(4), 816–829.
    [Google Scholar]
  20. Huddy, L., & Terkildsen, N. (1993a). The consequences of gender stereotypes for women candidates at different levels and types of office. Political Research Quarterly, 46(3), 503–525.
    [Google Scholar]
  21. Huddy, L., & Terkildsen, N. (1993b). Gender stereotypes and the perception of male and female candidates. American Journal of Political Science, 119–147.
    [Google Scholar]
  22. Jamieson, K. H. (1995). Beyond the double bind: Women and leadership. Oxford University Press.
    [Google Scholar]
  23. Koch, J. W. (1999). Candidate gender and assessments of senate candidates. Social Science Quarterly, 80(1), 84–96.
    [Google Scholar]
  24. Koppensteiner, M., & Grammer, K. (2011). Body movements of male and female speakers and their influence on perceptions of personality. Personality and Individual Differences, 51(6), 743–747.
    [Google Scholar]
  25. Koppensteiner, M., Stephan, P., & Jäschke, J. P. M. (2016). Moving speeches: Dominance, trustworthiness and competence in body motion. Personality and Individual Differences, 94, 101–106.
    [Google Scholar]
  26. Li, J., Wang, C., Zhu, H., Mao, Y., Fang, H.-S., & Lu, C. (2018). Crowdpose: Efficient crowded scenes pose estimation and a new benchmark. arXiv preprint arXiv:1812.00324.
    [Google Scholar]
  27. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., … Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In European conference on computer vision (pp. 740–755).
    [Google Scholar]
  28. Mehrabian, A. (1972). Nonverbal Communication. New Brunswick: Aldine Transaction.
    [Google Scholar]
  29. Pollick, F. E., Kay, J. W., Heim, K., & Stringer, R. (2005). Gender recognition from point-light walkers. Journal of Experimental Psychology: Human Perception and Performance, 31(6), 1247.
    [Google Scholar]
  30. Sanbonmatsu, K., & Dolan, K. (2009). Do gender stereotypes transcend party?Political Research Quarterly, 62(3), 485–494.
    [Google Scholar]
  31. Sapiro, V., Cramer Walsh, K., Strach, P., & Hennings, V. (2011). Gender, context, and television advertising: A comprehensive analysis of 2000 and 2002 House races. Political Research Quarterly, 64(1), 107–119.
    [Google Scholar]
  32. Schneider, M. C., & Bos, A. L. (2014). Measuring stereotypes of female politicians. Political Psychology, 35(2), 245–266.
    [Google Scholar]
  33. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815–823).
    [Google Scholar]
  34. Simon, T., Joo, H., Matthews, I., & Sheikh, Y. (2017). Hand keypoint detection in single images using multiview bootstrapping. In CVPR.
    [Google Scholar]
  35. Squire, P. (1992). The theory of legislative institutionalization and the california assembly. Journal of Politics, 54(4), 1026–1054.
    [Google Scholar]
  36. Wasike, B. (2019). Gender, nonverbal communication, and televised debates: a case study analysis of Clinton and Trump’s nonverbal language during the 2016 town hall debate. International Journal of Communication, 13, 26.
    [Google Scholar]
  37. Winter, N. J. (2010). Masculine Republicans and feminine Democrats: Gender and Americans’ explicit and implicit images of the political parties. Political Behavior, 32(4), 587–618.
    [Google Scholar]
  38. Xiu, Y., Li, J., Wang, H., Fang, Y., & Lu, C. (2018). Pose Flow: Efficient online pose tracking. In Bmvc.
    [Google Scholar]
  39. Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499–1503.
    [Google Scholar]
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