Social Media Users' Perceptions and Responses to Virtual Social Media Influencers
Recent technological innovations like artificial intelligence and computer-generated imagery have paved the way for a new type of social media influencers termed virtual influencers (VIs), which are defined as digital, anthropomorphic social media personae that are controlled by humans or AI software. While VIs are gaining in numbers and popularity, our understanding of the VI phenomenon is extremely limited, and it remains unclear whether, how, and why VIs can be effective brand endorsers. Drawing on the Persuasion Knowledge Model (PKM), this project aims to examine (1) social media users’ responses and skepticism toward VIs vs. human influencers (HIs); (2) their responses and skepticism toward sponsored posts by VIs vs. HIs; and (3) the role of skepticism in mediating the effects of influencer type on consumers’ attitude toward the promoted brand. A multi-method approach is used, including a content analysis of social media comments and posts, a computational analysis of said data, and an online experiment.
BIPOC Media Suppliers and Inequities in Advertising Metrics
This project will address the diversity-equity-inclusion issues in the current advertising media supplier field and systemic inequities in the domain of advertising media planning/pricing/buying. It specifically aims: (1) to examine the inequities in the current media planning/pricing/buying practice primarily based on what we’d call the “breadth” metric; (2) to develop an alternative “depth” metric that overcomes the limitations and equity issues of the “breadth” metric; and (3) to address the inequities in the inclusion, valuation, and pricing of BIPOC or subcultural group media suppliers and creators in advertising across different media contexts. A multimethod approach is used, including in-depth interviews and a survey with consumers and computational research approach analyzing social media data.
Companies Getting Political: Public-Company Identity Similarity and Its Influences on Public Reactions to Corporate Social Advocacy Initiatives
Today’s corporations are moving beyond their traditional social responsibility programs and making advocacy stances on sociopolitical issues. As many of the sociopolitical issues are divisive in nature, corporate social advocacy (CSA) initiatives tend to attract certain stakeholder groups but potentially alienate other groups at the same time. In this context, the extent to which publics perceive a company’s CSA stance to be consistent with or opposite to their own stances and value would become the basis to categorize the company as “our side” or the “opposing side,” and form identity similarity perceptions. Such perceptions are likely to lead publics to form or change their attitudes and behaviors regarding the involved company. In order to advance our understanding of the risks and rewards of CSA, it is critically important to examine public perception of identity similarity regarding companies involved in CSA and its impact on publics’ attitudinal and behavioral reactions to CSA. Applying the social identity theory, this project investigates publics’ responses to CSA initiatives and a theoretical mechanism mediating the responses by focusing on the role of public-company identity similarity perception.
Trust and Its Influence on Advertising Processes and Effects – Computational Research Applying the Trust Scores in Social Media (TSM) Algorithm
Applying trust theory and our 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. We test and demonstrate 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.
Influence of Consumers’ Affective States on Ad Attention and Evaluation
This project examines the influence of consumers’ temporary affective states during ad exposure on their selective attention to and evaluation of different types of ads that are categorized based on theoretically-derived attention-grabbing 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’ Reactions
This project examines strategies adopted by 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’ reactions to different kinds of communication patterns.
Publics’ Emotional Reactions and Acceptance of Organizational Crisis Response: Case of Boeing 737 MAX Crisis
This project examines publics’ emotional reactions to a crisis, and the impacts of such emotions on their acceptance of organizational crisis response communications, using computational analysis of the real-world example of the Boeing 737 MAX crisis.
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.
Gender-Based Ad Targeting and Consumer Reactions
By analyzing Twitter data, this project aims at addressing the question of ad-context congruity effects with a particular focus on gender-based ad targeting and gender-targeted ad messages.