Title

Identifying influencers on social media

Document Type

Article

Source of Publication

International Journal of Information Management

Publication Date

2-1-2021

Abstract

© 2020 Elsevier Ltd 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.

ISSN

0268-4012

Publisher

Elsevier Ltd

Volume

56

First Page

102246

Disciplines

Business | Computer Sciences | Social and Behavioral Sciences

Keywords

Big data, Influencers, Market mavens, Social media, Twitter

Scopus ID

85091964781

Indexed in Scopus

yes

Open Access

no

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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