Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research

Atanu Roy, Jisu Huh, Alexander Pfeuffer, Jaideep Srivastava

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Trust is an important factor, particularly in viral/social advertising, and computing trust scores for individual users of a social network is crucial for several applications in the advertising research and practice. However, research on trust in the advertising field has been limited, and the application of computational trust to advertising research using big data is rare. Addressing the gap in the research literature, this study proposed and empirically tested a new social media analytics method, the Trust Scores in Social Media (TSM) algorithm, for measuring individual users' trust levels in a social network. TSM proposes the concept of negatively reinforced trust scores and introduces two complementary measures of trust, trustingness, and trustworthiness. Another unique and important element in the TSM algorithm is the incorporation of trust-decision involvement to adjust trust scores depending on the level of trust-decision involvement of different networks. Using small survey data and big data from social networks, this study demonstrated the effectiveness of the TSM algorithm. Various applications of the TSM algorithm to viral/social advertising research and practice are also discussed.

Original languageEnglish
Pages (from-to)269-282
Number of pages14
JournalJournal of Advertising
Volume46
Issue number2
DOIs
Publication statusPublished - 3 Apr 2017
Externally publishedYes

Fingerprint

social media
Marketing
social advertising
social network
Social media
trustworthiness
Big data
Social networks

ASJC Scopus subject areas

  • Business and International Management
  • Communication
  • Marketing

Cite this

Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research. / Roy, Atanu; Huh, Jisu; Pfeuffer, Alexander; Srivastava, Jaideep.

In: Journal of Advertising, Vol. 46, No. 2, 03.04.2017, p. 269-282.

Research output: Contribution to journalArticle

@article{86e82c4919f141b0a5b8ffbeaf7f6f3c,
title = "Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research",
abstract = "Trust is an important factor, particularly in viral/social advertising, and computing trust scores for individual users of a social network is crucial for several applications in the advertising research and practice. However, research on trust in the advertising field has been limited, and the application of computational trust to advertising research using big data is rare. Addressing the gap in the research literature, this study proposed and empirically tested a new social media analytics method, the Trust Scores in Social Media (TSM) algorithm, for measuring individual users' trust levels in a social network. TSM proposes the concept of negatively reinforced trust scores and introduces two complementary measures of trust, trustingness, and trustworthiness. Another unique and important element in the TSM algorithm is the incorporation of trust-decision involvement to adjust trust scores depending on the level of trust-decision involvement of different networks. Using small survey data and big data from social networks, this study demonstrated the effectiveness of the TSM algorithm. Various applications of the TSM algorithm to viral/social advertising research and practice are also discussed.",
author = "Atanu Roy and Jisu Huh and Alexander Pfeuffer and Jaideep Srivastava",
year = "2017",
month = "4",
day = "3",
doi = "10.1080/00913367.2017.1297272",
language = "English",
volume = "46",
pages = "269--282",
journal = "Journal of Advertising",
issn = "0091-3367",
publisher = "M.E. Sharpe Inc.",
number = "2",

}

TY - JOUR

T1 - Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research

AU - Roy, Atanu

AU - Huh, Jisu

AU - Pfeuffer, Alexander

AU - Srivastava, Jaideep

PY - 2017/4/3

Y1 - 2017/4/3

N2 - Trust is an important factor, particularly in viral/social advertising, and computing trust scores for individual users of a social network is crucial for several applications in the advertising research and practice. However, research on trust in the advertising field has been limited, and the application of computational trust to advertising research using big data is rare. Addressing the gap in the research literature, this study proposed and empirically tested a new social media analytics method, the Trust Scores in Social Media (TSM) algorithm, for measuring individual users' trust levels in a social network. TSM proposes the concept of negatively reinforced trust scores and introduces two complementary measures of trust, trustingness, and trustworthiness. Another unique and important element in the TSM algorithm is the incorporation of trust-decision involvement to adjust trust scores depending on the level of trust-decision involvement of different networks. Using small survey data and big data from social networks, this study demonstrated the effectiveness of the TSM algorithm. Various applications of the TSM algorithm to viral/social advertising research and practice are also discussed.

AB - Trust is an important factor, particularly in viral/social advertising, and computing trust scores for individual users of a social network is crucial for several applications in the advertising research and practice. However, research on trust in the advertising field has been limited, and the application of computational trust to advertising research using big data is rare. Addressing the gap in the research literature, this study proposed and empirically tested a new social media analytics method, the Trust Scores in Social Media (TSM) algorithm, for measuring individual users' trust levels in a social network. TSM proposes the concept of negatively reinforced trust scores and introduces two complementary measures of trust, trustingness, and trustworthiness. Another unique and important element in the TSM algorithm is the incorporation of trust-decision involvement to adjust trust scores depending on the level of trust-decision involvement of different networks. Using small survey data and big data from social networks, this study demonstrated the effectiveness of the TSM algorithm. Various applications of the TSM algorithm to viral/social advertising research and practice are also discussed.

UR - http://www.scopus.com/inward/record.url?scp=85015674705&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015674705&partnerID=8YFLogxK

U2 - 10.1080/00913367.2017.1297272

DO - 10.1080/00913367.2017.1297272

M3 - Article

AN - SCOPUS:85015674705

VL - 46

SP - 269

EP - 282

JO - Journal of Advertising

JF - Journal of Advertising

SN - 0091-3367

IS - 2

ER -