Personality-Based Content Engineering for Rich Digital Media

Posted: 16 May 2019

See all articles by Haris Krijestorac

Haris Krijestorac

University of Texas at Austin, Red McCombs School of Business, Students

Rajiv Garg

University of Texas at Austin - Department of Information, Risk and Operations Management

Maytal Saar-Tsechansky

University of Texas at Austin

Date Written: April 4, 2019

Abstract

Given consumer resistance to advertising, firms have turned to rich digital media, such as videos and photos, to attract attention and boost brand awareness. Although extant research may help firms promote these media more effectively once they are released, the marketing process truly begins with the creation of the media. Thus, firms and content creators may benefit from understanding what media is likely to achieve greater popularity, based on its content features. We develop an approach to understand the role of content on the consumption of online videos, using a unique dataset including 16,414 videos from 363 YouTube channels. We introduce an algorithm to identify content features of online videos that predict high performance, and apply this approach both globally and on a channel-level. Our approach categorizes videos by their consumption relative to comparable videos, and leverages random forests to identify content features associated with their consumption level. We test this approach through an analysis of the personality of transcribed audio associated with speech-driven online videos, using NLP to assess the extent to which video captions exhibit each of the “big five” personality traits. Analysis reveals that videos corresponding to high-performing personalities are associated with a statistically significant increase in views, offering prescriptive insights for content engineering.

Suggested Citation

Krijestorac, Haris and Garg, Rajiv and Saar-Tsechansky, Maytal, Personality-Based Content Engineering for Rich Digital Media (April 4, 2019). Available at SSRN: https://ssrn.com/abstract=3366561

Haris Krijestorac (Contact Author)

University of Texas at Austin, Red McCombs School of Business, Students ( email )

Austin, TX
United States

Rajiv Garg

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

HOME PAGE: http://www.RajivGarg.org

Maytal Saar-Tsechansky

University of Texas at Austin ( email )

Austin, TX 78712
United States

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