IDEAS home Printed from https://ideas.repec.org/a/ucp/jacres/doi10.1086-714517.html
   My bibliography  Save this article

Application of Automated Text Analysis to Examine Emotions Expressed in Online Support Groups for Quitting Smoking

Author

Listed:
  • Erin A. Vogel
  • Cornelia (Connie) Pechmann

Abstract

Online support groups offer social support and an outlet for expressing emotions when dealing with health-related challenges. This study examines whether automated text analysis of emotional expressions using Linguistic Inquiry and Word Count (LIWC) can identify emotions related to abstinence expressed in online support groups for quitting smoking, suggesting promise for offering targeted mood management to members. The emotional expressions in 1 month of posts by members of 36 online support groups were related to abstinence at month end. Using the available LIWC dictionary, posts were scored for overall positive emotions, overall negative emotions, anxiety, anger, sadness, and an upbeat emotional tone. Greater expressions of negative emotions, and specifically anxiety, related to nonabstinence, while a more upbeat emotional tone related to abstinence. The results indicate that automated text analysis can identify emotions expressed in online support groups for quitting smoking and enable targeted delivery of mood management to group members.

Suggested Citation

  • Erin A. Vogel & Cornelia (Connie) Pechmann, 2021. "Application of Automated Text Analysis to Examine Emotions Expressed in Online Support Groups for Quitting Smoking," Journal of the Association for Consumer Research, University of Chicago Press, vol. 6(3), pages 315-323.
    • Handle: RePEc:ucp:jacres:doi:10.1086/714517
    DOI: 10.1086/714517
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1086/714517
    Download Restriction: Access to the online full text or PDF requires a subscription.
    File URL: http://dx.doi.org/10.1086/714517
    Download Restriction: Access to the online full text or PDF requires a subscription.
    File URL: https://libkey.io/10.1086/714517?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    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:ucp:jacres:doi:10.1086/714517. 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: Journals Division (email available below). General contact details of provider: https://www.journals.uchicago.edu/JACR .

    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.