Xinchen Yu

Xinchen Yu serves as an Assistant Professor of Practice in the Department of Computer Science at the University of Arizona, maintaining an office in GS 829. Holding a Ph.D. from the University of North Texas, their research bridges computational methodologies with social science inquiry to address contemporary digital society challenges.

Dr. Yu's scholarly focus centers on natural language processing and computational social science, with specialized expertise in analyzing online discourse dynamics. Their work investigates hate speech propagation, counterspeech effectiveness, conversation incivility measurement, and cross-cultural information diffusion patterns on social media platforms. Methodologically, they employ advanced NLP techniques, behavioral modeling, and causal inference frameworks to develop predictive systems for online conflict resolution and public sentiment analysis.

Analysis of Dr. Yu's publication trajectory (2020-2025) reveals a cohesive research program addressing societal impacts of digital communication. Their work consistently applies computational social science to critical issues including hate speech mitigation (with 4 publications on counterspeech taxonomy and forecasting), pandemic information ecology (3 studies on COVID-19 discourse), and transnational movement diffusion (2 analyses of #MeToo and cross-border information flow). A distinctive pattern emerges in their development of forecasting models for conversational incivility and hater reactions, combined with rigorous examination of linguistic features in cross-geographic message propagation.

Dr. Yu has not been mentioned to have received any specific scientific awards in the available documentation.

No information regarding student advising responsibilities or externally funded research grants is provided in the source materials. The absence of such details suggests either early-career status or non-disclosure in the scraped content.

There is no explicit reference to laboratory affiliations, research teams, or collaborative groups in the provided information, though their publication topics indicate engagement with interdisciplinary social computing research communities.