IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v45y2015i1p98-107.html
   My bibliography  Save this article

The Who-To-Follow System at Twitter: Strategy, Algorithms, and Revenue Impact

Author

Listed:
  • Ashish Goel

    (Stanford University, Stanford, California 94035)

  • Pankaj Gupta

    (Twitter Inc., San Francisco, California 94103)

  • John Sirois

    (Twitter Inc., San Francisco, California 94103)

  • Dong Wang

    (Twitter Inc., San Francisco, California 94103)

  • Aneesh Sharma

    (Twitter Inc., San Francisco, California 94103)

  • Siva Gurumurthy

    (Twitter Inc., San Francisco, California 94103)

Abstract

The who-to-follow system at Twitter is an algorithmic data product that recommends accounts for Twitter users to follow. Building the system involved algorithmic, analytics, operational, and experimental challenges; operations research and analytics techniques played a key role in resolving these challenges. This product has had significant direct impact on Twitter’s growth and the quality of its user engagement, and has also been a major driver of revenue. More than one-eighth of all new connections on the Twitter network are a direct result of this system, and a substantial majority of Twitter’s revenue comes from its promoted products, for which this system was a foundation. To place this contribution into perspective, Twitter is now a publicly traded company with a market capitalization of more than $30 billion, projected annual revenue of close to $1 billion, and more than 240 million active users.

Suggested Citation

  • Ashish Goel & Pankaj Gupta & John Sirois & Dong Wang & Aneesh Sharma & Siva Gurumurthy, 2015. "The Who-To-Follow System at Twitter: Strategy, Algorithms, and Revenue Impact," Interfaces, INFORMS, vol. 45(1), pages 98-107, February.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:1:p:98-107
    DOI: 10.1287/inte.2014.0784
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2014.0784
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2014.0784?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. Yaxuan Ran & Jiani Liu & Yishi Zhang, 2023. "Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 614-632, May.
    2. Federico Rossi & Gaia Rubera, 2021. "Measuring Competition for Attention in Social Media: National Women’s Soccer League Players on Twitter," Marketing Science, INFORMS, vol. 40(6), pages 1147-1168, November.

    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:inm:orinte:v:45:y:2015:i:1:p:98-107. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.