You reap where you sow: a trust-based approach to initial seeding for viral advertising

Abstract

This study examines the role of source trust in viral ad diffusion, specifically the impact of source trust on the reach and speed of ad diffusion. It tests the feasibility of using computer-algorithm-generated social media metrics, indicating the degree to which each person is trusted by others within a social network, for trust-based viral ad seeding strategy, and future research on viral advertising. Applying trust theory and the computational trust research approach using real-life viral ad cases, this study found that only a small proportion of social media users exposed to viral ads tend to contribute to ad diffusion, and those with higher trust scores make significantly stronger contributions to spreading viral ads faster and more broadly. Additionally, individuals’ source trust scores have stronger impact on the extent and speed of viral ad diffusion, especially in the situation where the ads contained socially-controversial messages. Theoretical and methodological contributions and practical implications are offered.

Keywords:

• Viral advertising
• social media advertising
• trust
• computational research
• computational trust

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Jisu Huh

Jisu Huh (Ph.D.) is professor and Mithun Endowed Chair in Advertising in the Hubbard School of Journalism and Mass Communication at the University of Minnesota. Her research covers a wide range of topics related to advertising and its effects, especially in the digital and social media contexts.

Hyejin Kim

Hyejin Kim (Ph.D.) is assistant professor in the College of Communication at DePaul University. Her research interests include social media advertising and electronic word-of-mouth (eWOM) with computational research approaches.

Bhavtosh Rath

Bhavtosh Rath (Ph.D. candidate) is currently pursuing a PhD in Computer Science from the University of Minnesota. He is a Computational Social Scientist doing research in the domain of fake news detection in social media. His research interests include Machine Learning, Data Mining and Social Network Analysis.

Xinyu Lu

Xinyu Lu (Ph.D.) is assistant professor in the School of Journalism and Communication at Shanghai International Studies University. Her research interests include consumer-brand engagement in social media, computational advertising research and digital advertising effects.

Jaideep Srivastava

Jaideep Srivastava (Ph.D.) is professor of Computer Science and Engineering at the University of Minnesota. His research is in machine learning with applications to social media and healthcare. He is a Fellow of the IEEE and recipient of the Lifetime Distinguished Contributions Award from PAKDD, for his contributions to the field of analytics, data mining, and machine learning.