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How Much Influencer Marketing Is Undisclosed? Evidence from Twitter

Author

Listed:
  • Daniel Ershov
  • Yanting, He
  • Stephan Seiler

Abstract

We quantify the prevalence of undisclosed influencer posts on Twitter across a large set of brands based on a unique data set of over 100 million posts. We develop a novel method to detect undisclosed influencer posts and find that 96% of influencer posts are not disclosed as such. Despite stronger enforcement of disclosure regulations, the share of undisclosed posts decreases only slightly over time. Compared to disclosed posts, undisclosed posts tend to be associated with younger brands with a large Twitter following and are posted from smaller accounts that generate higher engagement per follower.

Suggested Citation

  • Daniel Ershov & Yanting, He & Stephan Seiler, 2023. "How Much Influencer Marketing Is Undisclosed? Evidence from Twitter," CESifo Working Paper Series 10743, CESifo.
    • Handle: RePEc:ces:ceswps:_10743
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp10743.pdf
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    References listed on IDEAS

    as
    1. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Matthew Mitchell, 2021. "Free ad(vice): internet influencers and disclosure regulation," RAND Journal of Economics, RAND Corporation, vol. 52(1), pages 3-21, March.
    3. Itay P. Fainmesser & Andrea Galeotti, 2021. "The Market for Online Influence," American Economic Journal: Microeconomics, American Economic Association, vol. 13(4), pages 332-372, November.
    4. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    5. Amy Pei & Dina Mayzlin, 2022. "Influencing Social Media Influencers Through Affiliation," Marketing Science, INFORMS, vol. 41(3), pages 593-615, May.
    6. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    social media; influencer marketing; advertising disclosure; consumer protection;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • M38 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Government Policy and Regulation

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