Advertising’s positive and negative effects on consumers

Professor Jisu Huh's research is multidisciplinary and constantly evolving to meet the challenges and problems rising from the technological, social, and cultural evolution of the fields. She directs a research team, Minnesota Computational Advertising Lab, bringing together students from the Hubbard School of Journalism and Mass Communication and the University of Minnesota Computer Science department. Some of their current research projects include the following:

Trust and Its Influence on Advertising Processes and Effects: Applying trust theory and their own Trust Scores in Social Media (TSM) Algorithm, this project examines the role of source trust in viral ad diffusion and rumor spread and counter-rumor campaign effects. The team tests and demonstrates 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 counter-rumor campaigns.

Contextual Targeting with Affects – Computational Research Approach Examining the Influence of Consumers’ Temporary Affects on Ad Attention: This project examines the influence of consumers’ temporary affective states during media consumption on their selective attention to and evaluation of different types of ads that are categorized based on theoretically-derived characteristics. An innovative computational research approach is used, cross-analyzing proxy measures of real-time affective fluctuation of TV viewers during the Super Bowl broadcast and their tweets regarding the ads aired during the Super Bowl.

Social Media Influencers’ Communication Patterns during the COVID-19 Pandemic and Followers’ Emotional Reactions: This project examines strategies adopted by different types of social media influencers dealing with the current pandemic crisis, to discover patterns or changes in their social media activities and posts during the COVID-19 pandemic, and followers’ emotional reactions to different kinds of communication patterns.

Emotions During the COVID-19 Pandemic and Impact on Information-Seeking and Sharing: This project examines general patterns of discrete emotions emerging at different stages of the COVID-19 pandemic among different groups of people, and relationships between different emotions and people’s COVID-19-related information-seeking and information-sharing on Twitter.

Recently Huh organized the Computational Advertising Research Thought Leadership Forum (TLF) to advance the emerging field of computational advertising research. This brought together prominent senior scholars, active junior scholars, and industry thought leaders across the fields of advertising, communication, marketing, computer science, law, and information and decision science. This project aimed at setting new research agenda, innovating methodological approaches, and expanding the application of the computational research approach to advertising practice and scholarship. This unique and innovative project resulted in research papers published in the Journal of Advertising Special Section “Advances in Computational Advertising” in 2020. These papers have been widely circulated and read across the academe and industry.