IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v6y2016i1p2158244016633738.html
   My bibliography  Save this article

Everyday Life as a Text

Author

Listed:
  • Michael Lahey

Abstract

This article explores how audience data are utilized in the tentative partnerships created between television and social media companies. Specially, it looks at the mutually beneficial relationship formed between the social media platform Twitter and television. It calls attention to how audience data are utilized as a way for the television industry to map itself onto the everyday lives of digital media audiences. I argue that the data-intensive monitoring of everyday life offers some measure of soft control over audiences in a digital media landscape. To do this, I explore “Social TV†—the relationships created between social media technologies and television—before explaining how Twitter leverages user data into partnerships with various television companies. Finally, the article explains what is fruitful about understanding the Twitter–television relationship as a form of soft control.

Suggested Citation

  • Michael Lahey, 2016. "Everyday Life as a Text," SAGE Open, , vol. 6(1), pages 21582440166, February.
  • Handle: RePEc:sae:sagope:v:6:y:2016:i:1:p:2158244016633738
    DOI: 10.1177/2158244016633738
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244016633738
    Download Restriction: no

    File URL: https://libkey.io/10.1177/2158244016633738?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
    ---><---

    Citations

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


    Cited by:

    1. Roberto Tommasetti & Rodrigo de Oliveira Leite & Vinicius Mothé Maia & Marcelo Alvaro da Silva Macedo, 2021. "Revisiting the Accounting Fraud Components: A Bottom-Up Approach Using the Twitter Platform," SAGE Open, , vol. 11(4), pages 21582440211, November.
    2. Altuğ Tanaltay & Amirreza Safari Langroudi & Raha Akhavan-Tabatabaei & Nihat Kasap, 2021. "Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter," SAGE Open, , vol. 11(2), pages 21582440211, April.
    3. Pedro Santander & Rodrigo Alfaro & Héctor Allende-Cid & Claudio Elórtegui & Cristian González, 2020. "Analyzing social media, analyzing the social? A methodological discussion about the demoscopic and predictive potential of social media," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(3), pages 903-923, June.

    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:sae:sagope:v:6:y:2016:i:1:p:2158244016633738. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.