Research
I study persuasion and the design of persuasive messages, campaigns, and tools, with empirical work anchored in health communication. My approach is computational at every level: my current agenda is organized around the role of generative AI in persuasive communication, along three axes — AI as an object of study, as a methodological tool, and as an intervention. At the core of my methodological program is multimodal computational analysis of communication content — combining LLM annotation, natural language processing, computer vision, and interpretable machine learning with experimental design and neurophysiological measures (fMRI, eye-tracking).
Programs of Work
1 · AI as a research object — studying AI-generated media
With collaborators at UC Davis and Northwestern, I examine the visual realism, misinformation potential, and surrealist signatures of photorealistic AI-generated images across platforms — how convincing they are to audiences, how their content circulates online, and what risks they pose to the broader information environment. Relevant outputs: Proceedings of CHI Extended Abstracts, 2025.
2 · Multimodal computational analysis for communication research
I specialize in multimodal computational analysis of communication content — combining LLM annotation, natural language processing, computer vision, and interpretable machine learning to ask new questions about how persuasive messages work and where they break down.
My dissertation, Multimodal Emotional Appeals in Anti-Vaping Videos: A Computational Approach to Fear–Hope Flow and Message Effectiveness, applies multimodal computational analysis across visual, auditory, and linguistic channels — with LLM-assisted annotation at the core — to track how fear and hope move across an anti-vaping clip frame by frame and to test whether this emotional flow predicts psychological reactance and behavioral intentions in young adults who vape. In a parallel project with Dr. Jiaying Liu, I use interpretable machine learning to identify the cognitive, social, and emotional “recipes” that make anti-vaping messages effective (manuscript ready, targeted for American Journal of Preventive Medicine). Another line uses LLM annotation at scale to detect systematic “surreal” signatures of AI-generated images (Computational Communication Research, 2026).
3 · Generative AI as a persuasive communication intervention
With Dr. Jiaying Liu, I run experiments testing whether generative AI can tailor anti-vaping messages across socioeconomic and value-based subgroups — and whether scale-first AI personalization comes at the cost of equitable persuasion. This project received the 2026 AEJMC Mass Communication and Society Division Student Research Award. I am also developing an ongoing anti-vaping AI chatbot intervention with the CHARM Lab. This line of work builds on my earlier study of how audiences perceive AI-generated content (Hong, Peng, & Williams, New Media & Society, 2020) — a foundational question for whether AI-driven communication interventions land with the people they aim to reach.
Related work in persuasion and digital media
Beyond the AI agenda, I also work on news, norms, and emotional engagement in digital media environments — including how news headlines, user comments, and mixed emotional appeals shape norm perceptions and engagement (see Publications below).
Peer-Reviewed Publications
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2026Liu, X., Lu, Y., Peng, Q., Qian, S., Peng, Y., & Shen, C. Seeing the surreal: Mapping surrealism in photorealistic AI-generated images using large language models. Computational Communication Research, 8(2), 1–48.
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2025Peng, Q., Lu, Y., Peng, Y., Qian, S., Liu, X., & Shen, C. Crafting synthetic realities: Examining visual realism and misinformation potential of photorealistic AI-generated images. CHI Extended Abstracts (CHI EA '25), 1–12. ACM.
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2020Hong, J., Peng, Q., & Williams, D. Are you ready for artificial Mozart and Skrillex? An experiment testing expectancy violation theory and AI music. New Media & Society, 23(7), 1920–1935.
Registered Report Accepted in Principle
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2026Rathje, S., Asimovic, N., …, Peng, Q., …, & Van Bavel, J. J. Testing the causal impact of social media reduction around the globe. Nature. Stage 1 RR Accepted
Manuscripts Under Review
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2026Worsdale, A., …, Peng, Q., …, & Liu, J. Depressive symptoms alter the predictive value of neural responses to vaping prevention messages in young adults who vape.
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2026Peng, Q., Duong, H., Shi, R., & Liu, J. Contradiction disrupts and reinforcement plateaus: The interplay of news and user comments on norm perceptions in the digital age.
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2026Ismail, I.*, Peng, Q.*, Liu, J., & Oh, V. Y. Specific mixed emotional headlines drive online media engagement over and above positivity and negativity.
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2026Gonzales, A. L., Wang, L. H., Kim, Y. W., & Peng, Q. Meso-level theorizing the digital divide: A model of institutional capacity for digital equity.
Manuscripts in Preparation
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2026Liu, J., Peng, Q., Malik, M., Wang, Y., Norton, E., Markey, C., Ye, T., & Sweet, L. H. Identifying optimal cognitive, social, and emotional profiles of anti-vaping messages for young adult vapers: Insights from interpretable machine learning analysis.
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2026Ye, T., …, Peng, Q., …, & Liu, J. Occipitoparietal response to vape packaging with food-cues indexes visuospatial salience and predicts future vaping frequency in young adults.
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2026Peng, Q. Mapping the discourse network of cervical cancer screening on Reddit: A BERTopic and ANTMN approach.
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2026Peng, Q. What do we measure when we measure emotion? A four-level framework for communication research.
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2026Peng, Q., & Liu, J. Generative AI for scalable message tailoring: Differential persuasive effects across socioeconomic and value-based subgroups.
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2026Zhao, S., Yu, H., Wang, Y., Peng, Q., Ye, T., Sweet, L., & Liu, J. A neural signature of vaping and smoking cues.
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2026Peng, Q., …, & Liu, J. Looking without thinking, feeling without looking: Discrete emotions drive visual attention and persuasion resistance through independent pathways in anti-smoking messages.
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2026Peng, Q., & Lu, Y. When modalities disagree: A computational framework for measuring cross-modal emotion divergence.
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2026Gonzales, A. L., Kim, Y. W., Wang, L. H., & Peng, Q. The future of digital equity.
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2026Peng, Q., & Nabi, R. Empowering young adults against early-onset cancer: Balancing fear, hope, and psychological reactance in health messaging.
Selected Conference Presentations
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2026JulTwo presentations at the Summer 2026 NIH Tobacco Regulatory Science (TRS) Meeting, Washington, D.C. — poster on generative AI for anti-vaping message tailoring across socioeconomic and value-based subgroups, and oral presentation on identifying optimal cognitive, social, and emotional profiles of anti-vaping messages via interpretable machine learning.
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2026JunFour papers presented at the 76th ICA Annual Conference, Cape Town, South Africa — on mixed-emotional headlines, anti-vaping ML profiling, mapping surrealism in AI images, and the third-person effect in social-appeal messages.
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2026MayGenerative AI for scalable message tailoring.
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2026MarOptimal profiles of anti-vaping messages via interpretable ML.
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2024NovCongruent and incongruent norms: The impact of news and user comments on norm perceptions in the digital age.
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2024SepMisinformation potential of AI-generated images (poster).
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2024JunA first analysis of the misinformation potential of AI-generated images.
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2019JunAre you ready for artificial Mozart and Skrillex?
For the complete list, see my CV.