Research

Research Focus

My research examines how different sources (i.e., CEOs, brands, citizens) communicate during societal crises—and how audiences respond emotionally, politically, and behaviorally. I apply AI-powered methodsto understand what drives engagement, support, or backlash.

In today’s sociopolitical climate, corporate silence can be seen as complicity, while speaking up may trigger division. My work investigates how message source (e.g., CEO vs. brand vs. citizens), style (e.g., emotional tone, symbolism, emoji use), and context (e.g., political communication, social crises) shape audience reactions on digital platforms.

My dissertation, Voices in Action: AI-Powered Insights from Corporate Messaging During Societal Crises, bridges political communication and computational social science. It includes three studies that apply advanced AI and NLP techniques to analyze more than 250,000 real-world social media interactions:

Article 1 – Emojis & SDG Messaging

Draws on Media Richness Theory and uses fine-tuned deep learning transformer models to classify emoji types in brand communication about the UN Sustainable Development Goals (SDGs), revealing that concrete visuals increase engagement. → Accepted for EMAC 2025 and AMS 2025

Article 2 – CEO Voice During War

Applies machine learning-based emotion classification and framing analysis to show that CEO messages during the Russia–Ukraine war outperform brand messages in driving public engagement—especially when emotionally expressive (Executive Symbolism Theory). → Published in Journal of Public Policy & Marketing

Article 3 – Corporate Stances in Divided Times

Examines how shifts in corporate positioning on sensitive societal topics influence public reactions across ideological lines. Using advanced AI text analytics and causal inference techniques, this study uncovers how audiences interpret and emotionally respond to perceived inconsistencies in brand commitments during moments of societal tension. → Targeting Journal of Retailing

AI Techniques & Analytics Methods I Use

Zero-shot and fine-tuned transformer models (e.g., BERT, RoBERTa)
Sentiment, emotion, stance, and moral language classifiers
Human-in-the-loop AI agents for classification and content generation
Multimodal analysis across text, emojis, and platform-specific engagement
Experimental design using AI-generated stimuli for field or lab testing
Multi-agent chatbot framework to simulate persuasive brand-consumer interactions in controlled experiments
Role-specific agents (e.g., sentiment adjuster, content rewriter, ethical watchdog) for dynamic message generation
Mechanism modeling using causal inference and emotion–moral–ideological alignment pathways to uncover why and how audience reactions unfold

Purpose