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

9 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

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ASJC Scopus subject areas

  • Business and International Management
  • Communication
  • Marketing

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