Are we who we follow? Computationally analyzing human personality and brand following on Twitter

Abstract

In this study, we present a computational method for analyzing the congruence between personality of a brand’s Twitter account and the personality of their followers. We investigated attachment to brands on Twitter by computing personalities through a machine-learned computational analysis of Twitter postings rather than traditional personality tests. By studying three different brands, results revealed that on average, brand followers have personalities that are more congruent with the personality of brands they follow compared to users that do not follow those brands. Taking these findings into consideration, we discuss some considerations for advertising researchers and practitioners, as well as provide a new tool, the Brand Analytics Environment (BAE), to allow individuals without computer programming backgrounds to conduct this method themselves.

Keywords:

• Brand personality
• social media analytics
• computational methods
• Twitter
• tool
• brand analytics environment (BAE)
• audience analysis

Additional information

Notes on contributors

Joseph T. Yun

Joseph T. Yun, PhD, is the leader of the Social Research and Technology Innovation Lab at the University of Illinois at Urbana-Champaign, and conducts research in the realm of computational advertising.

Utku Pamuksuz

Utku Pamuksuz, PhD, is a specialized faculty member of IS/Data Analytics at the Gies College of Business, University of Illinois at Urbana-Champaign. His research focuses on big data, machine learning and predictive model development on digital platforms.

Brittany R. L. Duff

Brittany R. L. Duff, PhD, is an associate professor of advertising at the Charles Sandage Department of Advertising, University of Illinois at Urbana-Champaign. She is affiliated with the Institute for Communications Research and the Beckman Institute for Advanced Science and Technology. Her research is on advertising attention and perception.