Identifying influencers on social media. (February 2021)

• Record Type: Journal Article
• Authors: Harrigan, Paul; Daly, Timothy M.; Coussement, Kristof; Lee, Julie A.; Soutar, Geoffrey N.; Evers, Uwana
• Is Part Of: International journal of information management. Volume 56(2021)
• Publication Date: 2021-02
• Subjects: Influencers -- Market mavens -- Big data -- Social media -- Twitter; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2020.102246
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: Identifying influencers, of which market mavens are a key type, is a fundamental task for marketers. We have used a range of social media-based metrics to identify market mavens in a dataset combining over half a million Tweets with self-report survey data. Mavens have more followers, post more often, and use hashtags more often than non-mavens. Interestingly, mavens also write in a less readable way. 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.