IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/89047.html
   My bibliography  Save this paper

Slanted images: Measuring nonverbal media bias

Author

Listed:
  • Boxell, Levi

Abstract

I build a dataset of over one million images used on the front page of websites around the 2016 election period. I then use machine-learning tools to detect the faces of politicians across the images and measure the nonverbal emotional content expressed by each politician. Combining this with data on the partisan composition of each website’s users, I show that websites portray politicians that align with the partisan preferences of their users with more positive emotions. I also find that nonverbal coverage by Republican-leaning websites was not consistent over the 2016 election, but became more favorable towards Donald Trump after he clinched the Republican nomination.

Suggested Citation

  • Boxell, Levi, 2018. "Slanted images: Measuring nonverbal media bias," MPRA Paper 89047, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:89047
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/89047/1/MPRA_paper_89047.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gregory J. Martin & Ali Yurukoglu, 2017. "Bias in Cable News: Persuasion and Polarization," American Economic Review, American Economic Association, vol. 107(9), pages 2565-2599, September.
    2. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    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. Aromi, J. Daniel & Clements, Adam, 2021. "Facial expressions and the business cycle," Economic Modelling, Elsevier, vol. 102(C).

    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. Nathan Canen & Kristopher Ramsay, 2023. "Quantifying Theory in Politics: Identification, Interpretation and the Role of Structural Methods," Papers 2302.01897, arXiv.org.
    2. Thomas Fujiwara & Karsten Müller & Carlo Schwarz, 2021. "The Effect of Social Media on Elections: Evidence from the United States," NBER Working Papers 28849, National Bureau of Economic Research, Inc.
    3. Brian Knight & Ana Tribin, 2019. "The Limits of Propaganda: Evidence from Chavez’s Venezuela," Journal of the European Economic Association, European Economic Association, vol. 17(2), pages 567-605.
    4. Garz, Marcel & Szucs, Ferenc, 2023. "Algorithmic selection and supply of political news on Facebook," Information Economics and Policy, Elsevier, vol. 62(C).
    5. Leonardo Bursztyn & Aakaash Rao & Christopher Roth & David Yanagizawa-Drott, 2023. "Opinions as Facts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1832-1864.
    6. Bursztyn, Leonardo & Rao, Akaash & Roth, Christopher & Yanagizawa-Drott, David, 2020. "Misinformation during a Pandemic," The Warwick Economics Research Paper Series (TWERPS) 1274, University of Warwick, Department of Economics.
    7. Nicola Mastrorocco & Arianna Ornaghi, 2020. "Who Watches the Watchmen? Local News and Police Behavior in the United States," Trinity Economics Papers tep0720, Trinity College Dublin, Department of Economics, revised Nov 2020.
    8. Yeon-Koo Che & Konrad Mierendorff, 2019. "Optimal Dynamic Allocation of Attention," American Economic Review, American Economic Association, vol. 109(8), pages 2993-3029, August.
    9. Darya Korlyakova, 2021. "Learning about Ethnic Discrimination from Different Information Sources," CERGE-EI Working Papers wp689, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    10. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    11. Brian Knight & Ana Tribin, 2022. "Opposition Media, State Censorship, and Political Accountability: Evidence from Chavez’s Venezuela," The World Bank Economic Review, World Bank, vol. 36(2), pages 455-487.
    12. Thomas Fujiwara & Karsten Müller & Carlo Schwarz, 2021. "The Effect of Social Media on Elections: Evidence from the United States," NBER Working Papers 28849, National Bureau of Economic Research, Inc.
    13. Marcel Garz & Jonna Rickardsson, 2023. "Ownership and media slant: Evidence from Swedish newspapers," Kyklos, Wiley Blackwell, vol. 76(1), pages 18-40, February.
    14. Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(C).
    15. Jacopo Perego & Sevgi Yuksel, 2022. "Media Competition and Social Disagreement," Econometrica, Econometric Society, vol. 90(1), pages 223-265, January.
    16. Sarah Moshary, 2020. "Price discrimination in political advertising: Evidence from the 2012 presidential election," RAND Journal of Economics, RAND Corporation, vol. 51(3), pages 615-649, September.
    17. Junze Sun & Arthur Schram & Randolph Sloof, 2019. "A Theory on Media Bias and Elections," Tinbergen Institute Discussion Papers 19-048/I, Tinbergen Institute.
    18. Grossman, Gene M. & Helpman, Elhanan, 2023. "Electoral competition with fake news," European Journal of Political Economy, Elsevier, vol. 77(C).
    19. Raymond, Collin & Taylor, Sarah, 2021. "“Tell all the truth, but tell it slant”: Documenting media bias," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 670-691.
    20. Elena Esposito & Tiziano Rotesi & Alessandro Saia & Mathias Thoenig, 2023. "Reconciliation Narratives: The Birth of a Nation after the US Civil War," American Economic Review, American Economic Association, vol. 113(6), pages 1461-1504, June.

    More about this item

    Keywords

    media bias; images; emotions; nonverbal; polarization;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • H0 - Public Economics - - General
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:89047. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.