Research Themes

As a science communication scholar with expertise in deliberative democracy and computational social science, my research programs focus on two key objectives: identifying the sociopolitical mechanisms that lead to inequity in science and technology communication and developing deliberation technologies to empower communities. My work advances theories across communication, political science, and computer/information sciences while striving to create practical solutions that promote equity and civic engagement.

Uncovering Inequity in Science and Technology Communication

Digital platforms have expanded access to scientific knowledge, but they can also reproduce biases. My research examines how these biases manifest across social groups, content creators, and AI systems. Studying 14,000+ YouTube videos, I introduced the concept of “segregated inclusion”—where diverse voices exist but remain structurally marginalized. My work also explores gender representation in science communication, showing that women are more often linked to themes of care and loyalty, while men are associated with authority. These biases extend to visual portrayals, influencing how science is framed across platforms. My ongoing work examines how AI systems may amplify such disparities and provide opportunities for cross-cultural empathy, particularly in science and medical information for non-English speakers.

Selected Publications

• Chen, K. Jeon, J., & Zhou, Y. (2023). A critical appraisal of diversity in digital knowledge production: Segregated inclusion on YouTube. New Media & Society, 25(11), 2855-2876.
• Chen, K., Duan, Z., & Kim, S. (2024). Uncovering gender stereotypes in controversial science discourse: Evidence from computational text and visual analyses across digital platforms. Journal of Computer-Mediated Communication , 29(1).
• Chen, K., Shao, A., Burapacheep, J., & Li, Y. (2024). Conversational AI and equity through assessing GPT-3's communication with diverse social groups on contentious topics. Scientific Reports, 14(1), Article 1561.

Empowering Communities in Science & Technology Policymaking

To address inequities, my team develops deliberation technologies—communication strategies and digital tools that enhance public participation in policymaking. In Dane County, Wisconsin, we conducted a field experiment showing that identity-based storytelling fosters stronger engagement in environmental policy discussions than fact-based messaging. Beyond in-person forums, my research explores digital crowdsourcing as a tool for ongoing civic engagement. My team developed the Wisconsin Climate Community Solutions platform, allowing residents to map environmental concerns and share personal experiences. This work demonstrates that deliberation fosters civic empowerment and government responsiveness, with evidence from the U.S., Ghana, and China.

Selected Publications / Open-Source Platforms

• Molder, A. L., Villanueva, I. I., & Chen, K. (2024). The impact of public deliberation and identity- based storytelling on civic empowerment among latinx communities on environmental issues. Environmental Communication, 18(8), 1092-1109.
• Digital Crowdsourcing Platform: https://ccs.mysocialpinpoint.com
• Community Knowledge Map: https://connect.doit.wisc.edu/knowledge-map

Advancing Computational Social Science Methods

My work contributes to computational social science by integrating advanced methods to study public discourse. My work expands beyond text analysis by using Computer Vision & NLP to analyze large-scale, cross-platform discussions, combining computational models with qualitative research to enrich public opinion studies, and collaborating across disciplines (computer science, mathematics, geosciences) to apply data science in innovative ways.

Selected Publications

• Chen, K., Kim, S. J., Gao, Q., & Raschka, S. (2022). Visual framing of science conspiracy videos: Integrating machine learning with communication theories to study the use of color and brightness. Computational Communication Research, 4(1), 98-134.
• Chen, K., & Aitamurto, T. (2019). Barriers for crowd’s impact in crowdsourced policymaking: Civic data overload and filter hierarchy. International Public Management Journal, 22(1), 99-126.
• Hou, X., Gao, S., Li, Q., Kang, Y., Chen, N., Chen, K., Rao, J., Ellenberg, J. S., & Patz, J. A. (2021). Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race. Proceedings of the National Academy of Sciences , 118(24), Article e2020524118.