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

Joseph T. Yun, Utku Pamuksuz, Brittany Duff

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)776-795
Number of pages20
JournalInternational Journal of Advertising
Volume38
Issue number5
DOIs
StatePublished - Jul 4 2019

Fingerprint

twitter
Computational methods
Computer programming
Marketing
personality
follower
personality test
programming
Twitter

Keywords

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

ASJC Scopus subject areas

  • Communication
  • Marketing

Cite this

Are we who we follow? Computationally analyzing human personality and brand following on Twitter. / Yun, Joseph T.; Pamuksuz, Utku; Duff, Brittany.

In: International Journal of Advertising, Vol. 38, No. 5, 04.07.2019, p. 776-795.

Research output: Contribution to journalArticle

@article{2097cedb6a554507875645d5cdba11d0,
title = "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, Twitter, audience analysis, brand analytics environment (BAE), computational methods, social media analytics, tool",
author = "Yun, {Joseph T.} and Utku Pamuksuz and Brittany Duff",
year = "2019",
month = "7",
day = "4",
doi = "10.1080/02650487.2019.1575106",
language = "English (US)",
volume = "38",
pages = "776--795",
journal = "International Journal of Advertising",
issn = "0265-0487",
publisher = "NTC Publications Ltd.",
number = "5",

}

TY - JOUR

T1 - Are we who we follow? Computationally analyzing human personality and brand following on Twitter

AU - Yun, Joseph T.

AU - Pamuksuz, Utku

AU - Duff, Brittany

PY - 2019/7/4

Y1 - 2019/7/4

N2 - 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.

AB - 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.

KW - Brand personality

KW - Twitter

KW - audience analysis

KW - brand analytics environment (BAE)

KW - computational methods

KW - social media analytics

KW - tool

UR - http://www.scopus.com/inward/record.url?scp=85061937674&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061937674&partnerID=8YFLogxK

U2 - 10.1080/02650487.2019.1575106

DO - 10.1080/02650487.2019.1575106

M3 - Article

AN - SCOPUS:85061937674

VL - 38

SP - 776

EP - 795

JO - International Journal of Advertising

JF - International Journal of Advertising

SN - 0265-0487

IS - 5

ER -