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Identifying influencers on social media

Author

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  • Harrigan, Paul
  • Daly, Timothy M.
  • Coussement, Kristof
  • Lee, Julie A.
  • Soutar, Geoffrey N.
  • Evers, Uwana

Abstract

The increased availability of social media big data has created a unique challenge for marketing decision-makers; turning this data into useful information. One of the significant areas of opportunity in digital marketing is influencer marketing, but identifying these influencers from big data sets is a continual challenge. This research illustrates how one type of influencer, the market maven, can be identified using big data. Using a mixed-method combination of both self-report survey data and publicly accessible big data, we gathered 556,150 tweets from 370 active Twitter users. We then proposed and tested a range of social-media-based metrics to identify market mavens. Findings show that market mavens (when compared to non-mavens) have more followers, post more often, have less readable posts, use more uppercase letters, use less distinct words, and use hashtags more often. These metrics are openly available from public Twitter accounts and could integrate into a broad-scale decision support system for marketing and information systems managers. These findings have the potential to improve influencer identification effectiveness and efficiency, and thus improve influencer marketing.

Suggested Citation

  • Harrigan, Paul & Daly, Timothy M. & Coussement, Kristof & Lee, Julie A. & Soutar, Geoffrey N. & Evers, Uwana, 2021. "Identifying influencers on social media," International Journal of Information Management, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:ininma:v:56:y:2021:i:c:s0268401220314456
    DOI: 10.1016/j.ijinfomgt.2020.102246
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    Citations

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    Cited by:

    1. Muñoz, María M. & Rojas-de-Gracia, María-Mercedes & Navas-Sarasola, Carlos, 2022. "Measuring engagement on twitter using a composite index: An application to social media influencers," Journal of Informetrics, Elsevier, vol. 16(4).
    2. Camilleri, Mark Anthony & Kozak, Metin, 2022. "Interactive engagement through travel and tourism social media groups: A social facilitation theory perspective," Technology in Society, Elsevier, vol. 71(C).
    3. Xing, Yunfei & Wang, Xiwei & Qiu, Chengcheng & Li, Yueqi & He, Wu, 2022. "Research on opinion polarization by big data analytics capabilities in online social networks," Technology in Society, Elsevier, vol. 68(C).
    4. Jose A. Flecha Ortiz & María Los M. Santos Corrada & Evelyn Lopez & Virgin Dones & Vivian Feliberty Lugo, 2023. "Don't make ads, make TikTok’s: media and brand engagement through Gen Z's use of TikTok and its significance in purchase intent," Journal of Brand Management, Palgrave Macmillan, vol. 30(6), pages 535-549, November.
    5. Tafesse, Wondwesen & Dayan, Mumin, 2023. "Content creators' participation in the creator economy: Examining the effect of creators’ content sharing frequency on user engagement behavior on digital platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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