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Reaching the tipping point: A critical analysis of the #deletefacebook movement

Author

Listed:
  • Bright, Laura F.

    (Stan Richards School of Advertising and Public Relations, Moody College of Communication, University of Texas at Austin, USA)

  • Sussman, Kristen Leah

    (Stan Richards School of Advertising & Public Relations, Moody College of Communication, University of Texas at Austin, USA)

  • Wilcox, Gary B.

    (Stan Richards School of Advertising and Public Relations, The University of Texas at Austin, USA)

Abstract

Social media platforms have sustained increased scrutiny for their data management practices, spread of misinformation and the creation of consumer echo chambers. Chief among these platforms is Facebook. Yet, consumption of Facebook continues to grow. To understand this paradox, we use a dataset of unstructured text data to identify patterns within user-generated content (UGC) using the #deletefacebook hashtag. Nearly 1.5 million observations were used to identify themes and help paint a picture of the broader consumer concern relating to Facebook. The results show a continuing interest in the #deletefacebook topic as measured by volume over time. Using machine learning techniques, the text miner results identified topics which include privacy, consumer trust and wellbeing and politics of data security. Fear of missing out (FoMO) is provided as a theoretical explanation for why people continue using social media sites like Facebook. Themes related to social media fatigue, data management and ethics were also found in the UGC.

Suggested Citation

  • Bright, Laura F. & Sussman, Kristen Leah & Wilcox, Gary B., 2021. "Reaching the tipping point: A critical analysis of the #deletefacebook movement," Journal of Data Protection & Privacy, Henry Stewart Publications, vol. 4(4), pages 424-435, September.
    • Handle: RePEc:aza:jdpp00:y:2021:v:4:i:4:p:424-435
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    More about this item

    Keywords

    Social media consumption; social media usage; Facebook; language; user-generated content; natural language processing; computational social science;
    All these keywords.

    JEL classification:

    • K2 - Law and Economics - - Regulation and Business Law

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