Measuring social media influencer index- insights from facebook, Twitter and Instagram

By Anuja Arora, Shivam Bansal, Chandrashekhar Kandpal, Reema Aswani, Yogesh Kumar Dwivedi. Journal of Retailing and Consumer Services, 2019

515 citations recorded by the source corpus.

Abstract The growth of social media has completely revamped the way people interact, communicate and engage. These platforms play a key role in facilitating greater outreach and influence. This study proposes a mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram. A set of features that determine the impact on the consumers are modelled using a regression approach. The underlying machine learning algorithms including Ordinary Least Squares (OLS), K-NN Regression (KNN), Support Vector Regression (SVR), and Lasso Regression models are adapted to compute a cumulative score in terms of influencer index. Findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers. Further, the ensemble of the four models resulted in the highest accuracy of 93.7% followed by the KNN regression with 93.6%. The study has implications across various domains of e-commerce, viral marketing, social media marketing and brand management wherein identification of key information propagators is essential. These influencer indices may further be utilized by e-commerce portals and brands for the purpose of social media promotion and engagement for larger outreach.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/260a60726f4e4294f6cec35fdf5432ea843efe46

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In Sociology and Political Science

Citations in the corpus

Influencer Marketing and Authenticity in Content Creation (2021, background): “Transparent authenticity also entails disclosing information about the contractual terms of the partnership with the particular brand, as well as posting unedited content.” with followers (Arora et al., 2019; C. Liu et al., 2020).”
How social media influencers affect behavioural intentions towards recommended brands: the role of emotional attachment and information value (2021, background): “…our findings, beyond selecting influencers from their number of followers or other observable metrics (e.g. number of likes, posting comments) (see Arora et al., 2019), brands should also consider choosing influencers who have the ability to or have developed a strong emotional attachment with…”
More Trust in Fewer Followers: Diverging Effects of Popularity Metrics and Green Orientation Social Media Influencers (2021, result): “Instagram was chosen as the platform for all studies because its metrics (likes and follower counts) indicate greater influencer success compared to similar metrics on other platforms (Arora et al., 2019).”