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

Abstract

Abstract

Television offers an enticing glimpse into the world, but its perspective is often skewed. When societal groups are systematically excluded from appearing on the screen, they lose the chance to represent their characteristics and interests. Recipients may then form distorted perceptions and attitudes towards those groups. Empirical research on the prevalence of such biases - especially across stations, time, and genre - has been limited by the effort of manual content analyses. We develop and validate a deep-learning based method for measuring age and gender of faces in video material. An analysis of approximately 16 million faces from six years of German mainstream TV across six stations is fused with existing program metadata indicating timing and genre of broadcasts, including advertisements. Multilevel regression models show a consistent and temporally stable discrimination against women and elderly people, along with a double discrimination of elderly women. A significant amount of variation across genres and systematic differences between public and private broadcasters furthermore indicate previously undocumented heterogeneity in the representation of societal groups on TV. We discuss potential implications of a genre-specific differentiation against the backdrop of societal trends.

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2022-02-01
2022-12-07
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References

  1. Agustsson, E., Timofte, R., Escalera, S., Baro, X., Guyon, I., & Rothe, R. (2017). Apparent and real age estimation in still images with deep residual regressors on Appa-Real Database. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 87–94. https://doi.org/10.1109/FG.2017.20
    [Google Scholar]
  2. Bandura, A. (2001). Social cognitive theory of mass communication. Media Psychology, 3, 265–299.
    [Google Scholar]
  3. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
    [Google Scholar]
  4. Becker, J., Maurer, T., Spittka, E., Benert, V., Weiß, H.-J., Beier, A., Hölig, S., Hasebrink, U., Volpers, H., Bernhard, U., Dammler, A., Blase, R., Langer, C., Walter, M., Feldhaus, D., Frerichmann, N., Holsten, C., Hein, D., #x98;die#x9C; medienanstalten – ALM GbR (Hrsg.), & VISTAS Verlag. (2018). Content-Bericht 2017 Forschung, Fakten, Trends.
    [Google Scholar]
  5. Baumann, S., & de Laat, K. (2012). Socially defunct: A comparative analysis of the underrepresentation of older women in advertising. Poetics, 40(6), 514–541. https://doi.org/10.1016/j.poetic.2012.08.002
    [Google Scholar]
  6. Campbell, D. E., & Wolbrecht, C. (2006). See Jane run: Women politicians as role models for adolescents. The Journal of Politics, 68(2), 233–247. https://doi.org/10.1111/j.1468-2508.2006.00402.x
    [Google Scholar]
  7. Cann, D. J., & Mohr, P. B. (2001). Journalist and source gender in Australian television news. Journal of Broadcasting & Electronic Media, 45(1), 162–174. https://doi.org/10.1207/s15506878jobem4501_10
    [Google Scholar]
  8. Chollet, F. & others. (2015). Keras. GitHub. https://github.com/fchollet/keras
    [Google Scholar]
  9. Collins, R. L. (2011). Content analysis of gender roles in media: Where are we now and where should we go?Sex Roles, 64(3–4), 290–298. https://doi.org/10.1007/s11199-010-9929-5
    [Google Scholar]
  10. Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017). Deformable convolutional networks. 2017 IEEE International Conference on Computer Vision (ICCV), 764–773. https://doi.org/10.1109/ICCV.2017.89
    [Google Scholar]
  11. Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., & Zafeiriou, S. (2019). RetinaFace: Single-stage dense face localisation in the wild. ArXiv:1905.00641 [Cs]. http://arxiv.org/abs/1905.00641
    [Google Scholar]
  12. Desmond, R., & Danilewicz, A. (2010). Women are on, but not in the news: Gender Roles in local television news. Sex Roles, 62(11–12), 822–829. https://doi.org/10.1007/s11199-009-9686-5
    [Google Scholar]
  13. Edström, M. (2018). Visibility patterns of gendered ageism in the media buzz: A study of the representation of gender and age over three decades. Feminist Media Studies, 18(1), 77–93. https://doi.org/10.1080/14680777.2018.1409989
    [Google Scholar]
  14. Eisend, M. (2010). A meta-analysis of gender roles in advertising. Journal of the Academy of Marketing Science, 38(4), 418–440. https://doi.org/10.1007/s11747-009-0181-x
    [Google Scholar]
  15. Fox, C. (2018). The scully effect: I want to believe in stem. https://seejane.org/wp-content/uploads/x-files-scully-effect-report-geena-davis-institute.pdf. Accessed 15Sep2019.
    [Google Scholar]
  16. Fryberg, S. A., & Townsend, S. M. (2008). The psychology of invisibility. In G.Adams, M.Biernat, N. R.Branscombe, C. S.Crandall, & L. S.Wrightsman (Eds.), Commemorating brown: The social psychology of racism and discrimination (pp. 173–193). American Psychological Association.
    [Google Scholar]
  17. Garvie, C., Bedoya, A., & Frankle, J. (2016). The perpetual line-up. Unregulated police face recognition in America. Georgetown Law Center on Privacy & Technology, October 18, 2016.
    [Google Scholar]
  18. Geena Davis Institute on Gender in the Media (2015). Cinema and Society: Shaping our worldview beyond the lens. Investigation on the impact of gender representation in Brazilian films. https://seejane.org/wp-content/uploads/cinema-and-society-investigation-of-the-impact-on-gender-representation-in-brazilian-films.pdf. Accessed 15Sep2019.
  19. Gerbner, G., & Gross, L. (1976). Living with television: The violence profile. Journal of Communication, 26(2), 173–199.
    [Google Scholar]
  20. Gerbner, G., & Signorielli, N. (1979). Women and minorities in television drama 1969–1978. The Annenberg School of Communication.
    [Google Scholar]
  21. Girshick, R. B. (2015). Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/ICCV.2015.169
    [Google Scholar]
  22. Haraldsson, A., & Wängnerud, L. (2019). The effect of media sexism on women’s political ambition: Evidence from a worldwide study. Feminist Media Studies, 19(4), 525–541. https://doi.org/10.1080/14680777.2018.1468797
    [Google Scholar]
  23. Hether, H. J., & Murphy, S. T. (2010). Sex roles in health storylines on prime time television: A content analysis. Sex Roles, 62(11–12), 810–821. https://doi.org/10.1007/s11199-009-9654-0
    [Google Scholar]
  24. Hill, K. (2020, January18). The secretive company that might end privacy as we know it. The New York Times. https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html
    [Google Scholar]
  25. Hjelmås, E., & Low, B. K. (2001). Face detection: A survey. Computer Vision and Image Understanding, 83(3), 236–274. https://doi.org/10.1006/cviu.2001.0921
    [Google Scholar]
  26. Huang, G. B., Mattar, M., Berg, T., & Learned-Miller, E. (2008, October). Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments. Workshop on faces in “real-life” images: Detection, alignment, and recognition. https://hal.inria.fr/inria-00321923
    [Google Scholar]
  27. Karkkainen, K., & Joo, J. (2021). FairFace: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 1548–1558.
    [Google Scholar]
  28. Kessler, E.-M., Schwender, C., & Bowen, C. E. (2010). The portrayal of older people’s social participation on German prime-time TV advertisements. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 65B(1), 97–106. https://doi.org/10.1093/geronb/gbp084
    [Google Scholar]
  29. Küchenhoff, E. & Bossmann, W. (1975). Die Darstellung der Frau und die Behandlung von Frauenfragen im Fernsehen: Eine empirische Untersuchung einer Forschungsgruppe der Universität Münster. Kohlkammer.
    [Google Scholar]
  30. Liebler, C. M., & Smith, S. J. (1997). Tracking gender differences: A comparative analysis of network correspondents and their sources. Journal of Broadcasting & Electronic Media, 41(1), 58–68. https://doi.org/10.1080/08838159709364390
    [Google Scholar]
  31. Lind, F., & Meltzer, C. E. (2020). Now you see me, now you don’t: Applying automated content analysis to track migrant women’s salience in German news. Feminist Media Studies, 1–18. https://doi.org/10.1080/14680777.2020.1713840
    [Google Scholar]
  32. Lukesch, H. & Schneider, I. (2004). Das Weltbild des Fernsehens: Eine Untersuchung der Sendungsangebote öffentlich-rechtlicher und privater Sender in Deutschland. Band 2: Theorie - Methode - Ergebnisse. Roderer.
    [Google Scholar]
  33. Matthes, J., Prieler, M., & Adam, K. (2016). Gender-Role portrayals in television advertising across the globe. Sex Roles, 75(7–8), 314–327. https://doi.org/10.1007/s11199-016-0617-y
    [Google Scholar]
  34. McCombs, M. E. (2007). Setting the agenda: The mass media and public opinion (Reprinted.). Polity Press.
    [Google Scholar]
  35. Medienanstalten. (2019). Interstate treaty on broadcasting and telemedia (Interstate Broadcasting Treaty).
    [Google Scholar]
  36. Najibi, M., Samangouei, P., Chellappa, R., & Davis, L. S. (2017). SSH: Single stage headless face detector. 2017 IEEE International Conference on Computer Vision (ICCV), 4885–4894. https://doi.org/10.1109/ICCV.2017.522
    [Google Scholar]
  37. Neverova, N., Alp Güler, R., & Kokkinos, I. (2018). Dense pose transfer. In V.Ferrari, M.Hebert, C.Sminchisescu, & Y.Weiss (Eds.), Computer Vision – ECCV 2018 (Vol. 11207, pp. 128–143). Springer International Publishing. https://doi.org/10.1007/978-3-030-01219-9_8
    [Google Scholar]
  38. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. Proceedings of the British Machine Vision Conference 2015, 41.1-41.12. https://doi.org/10.5244/C.29.41
    [Google Scholar]
  39. Prior, M. (2007). Post-broadcast democracy: How media choice increases inequality in political involvement and polarizes elections. Cambridge Univ. Press.
    [Google Scholar]
  40. Prieler, M., Kohlbacher, F., Hagiwara, S., & Arima, A. (2015). The representation of older people in television advertisements and social change: The case of Japan. Ageing and Society, 35(4), 865–887. https://doi.org/10.1017/S0144686X1400004X
    [Google Scholar]
  41. Prommer, E., & Linke, C. (2019). Ausgeblendet: Frauen im deutschen Film und Fernsehen. Herbert von Halem Verlag.
    [Google Scholar]
  42. R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
    [Google Scholar]
  43. Roth, J., Chaudhuri, S., Klejch, O., Marvin, R., Gallagher, A., Kaver, L., Ramaswamy, S., Stopczynski, A., Schmid, C., Xi, Z., & Pantofaru, C. (2020). Ava active speaker: An audio-Visual dataset for active Sspeaker detection. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4492–4496. https://doi.org/10.1109/ICASSP40776.2020.9053900
    [Google Scholar]
  44. Rothe, R., Timofte, R., & Gool, L. V. (2015). DEX: Deep EXpectation of apparent age from a single image. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 252–257. https://doi.org/10.1109/ICCVW.2015.41
    [Google Scholar]
  45. Rothe, R., Timofte, R., & Van Gool, L. (2018). Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision, 126(2), 144–157. https://doi.org/10.1007/s11263-016-0940-3
    [Google Scholar]
  46. Ruder, S. (2017). An overview of multi-task learning in deep neural networks. ArXiv:1706.05098 [Cs, Stat]. http://arxiv.org/abs/1706.05098
    [Google Scholar]
  47. United Nations, Department of Economic and Social Affairs, Population Division (2017).World Population Ageing 2017 - Highlights (ST/ESA/SER.A/397).
    [Google Scholar]
  48. Schneier, B. (2020, January20). Opinion | We’re Banning facial Recognition. We’re missing the point. The New York Times. https://www.nytimes.com/2020/01/20/opinion/facial-recognition-ban-privacy.html
    [Google Scholar]
  49. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. 815–823. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html
    [Google Scholar]
  50. Signorielli, N. (2004). Aging on television: Messages relating to gender, race, and occupation in prime time. Journal of Broadcasting & Electronic Media, 48(2), 279–301. https://doi.org/10.1207/s15506878jobem4802_7
    [Google Scholar]
  51. Sink, A., & Mastro, D. (2017). Depictions of gender on primetime television: A quantitative content analysis. Mass Communication and Society, 20(1), 3–22. https://doi.org/10.1080/15205436.2016.1212243
    [Google Scholar]
  52. Smith, S. L., Pieper, K. M., Granados, A., & Choueiti, M. (2010). Assessing gender-related portrayals in top-grossing G-rated films. Sex Roles, 62(11–12), 774–786. https://doi.org/10.1007/s11199-009-9736-z
    [Google Scholar]
  53. SWR. (2018). SWR Mitschnittdienst. https://swrservice.de/mitschnitt/
    [Google Scholar]
  54. Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. 1701–1708.
    [Google Scholar]
  55. Tuchman, G. (1978). The symbolic annihilation of women by the media. In G.Tuchman, A. K.Daniels, & J.Benét (Eds.), Hearth and home: Images of women in the mass media (pp. 3–38). Oxford University Press.
    [Google Scholar]
  56. Turner, J. S. (2011). Sex and the spectacle of music videos: An examination of the portrayal of race and sexuality in music videos. Sex Roles, 64(3–4), 173–191. https://doi.org/10.1007/s11199-010-9766-6
    [Google Scholar]
  57. Uchida, Y. (2021). Yu4u/age-gender-estimation [Jupyter Notebook]. https://github.com/yu4u/age-gender-estimation (Original work published 2017)
    [Google Scholar]
  58. Van Bauwel, S. (2018). Invisible golden girls? Post-feminist discourses and female ageing bodies in contemporary television fiction. Feminist Media Studies, 18(1), 21–33. https://doi.org/10.1080/14680777.2018.1409969
    [Google Scholar]
  59. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
    [Google Scholar]
  60. Weiß, H.J., Maurer, T. & Beier, A. (2020). ARD/ZDF-Programmanalyse 2019: Kontinuität und Wandel. Media Perspektiven 6/2020, 226-225.
    [Google Scholar]
  61. WDR. (2021). Geschäftsfelder—I-O - Mitschnittservice. WDR Mediagroup. https://wdr-mediagroup.com/geschaeftsfelder/i-o/mitschnittservice/
    [Google Scholar]
  62. Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). WIDER FACE: A face detection benchmark. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5525–5533. https://doi.org/10.1109/CVPR.2016.596x
    [Google Scholar]
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