Voice Analytics of Online Influencers

136 Pages Posted: 18 Feb 2021 Last revised: 28 Aug 2022

See all articles by Serim Hwang

Serim Hwang

Carnegie Mellon University

Xiao Liu

New York University (NYU) - Leonard N. Stern School of Business

Kannan Srinivasan

Carnegie Mellon University - David A. Tepper School of Business

Date Written: January 26, 2021

Abstract

In the rapidly growing influencer marketing industry, positive consumer sentiment toward sponsored content is critical for the success of sponsoring brands and influencers alike. Unfortunately, consumers tend to react more negatively to sponsored videos than to non-sponsored videos. This paper studies how influencers may attempt to curb the negative effect of sponsorship on consumer sentiment by modulating their vocal characteristics in sponsored videos relative to non-sponsored videos. We use deep learning methods to extract multi-modal 3V features (voice, visual, and verbal), and we use three causal identification strategies: instrumental variables, fuzzy regression discontinuity, and two-way fixed effects. We find that influencers decrease the average loudness of their voices in sponsored videos relative to equivalent non-sponsored videos, and the difference in average loudness partly mitigates the negative effect of sponsorship on consumer sentiment. Influencers are heterogeneous in the extent to which they decrease loudness in sponsored videos; for example, the difference in average loudness between sponsored and non-sponsored videos is smaller among influencers with more followers but larger among influencers with a steeper short-term increase in the number of Instagram followers. We discuss implications for four stakeholders: influencers, brands, consumers, and the Federal Trade Commission.

Keywords: influencer marketing, social media advertisements, voice analytics, affective computing, image recognition

Suggested Citation

Hwang, Serim and Liu, Xiao and Srinivasan, Kannan, Voice Analytics of Online Influencers (January 26, 2021). Available at SSRN: https://ssrn.com/abstract=3773825 or http://dx.doi.org/10.2139/ssrn.3773825

Serim Hwang

Carnegie Mellon University ( email )

Pittsburgh, PA
United States
4126282953 (Phone)

Xiao Liu

New York University (NYU) - Leonard N. Stern School of Business ( email )

Suite 9-160
New York, NY
United States

Kannan Srinivasan (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

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