One of our central research themes examines how digital media and AI are reshaping the scientific process in profound ways. Our work bridges science communication with rigorous, large-scale empirical analysis spanning all academic domains. We investigate how scientific knowledge is disseminated across digital environments, from social media platforms to online news outlets (ICWSM, 2020; ICWSM, 2023; CI, 2025a; EPJ Data Science, 2025; CSCW, 2025). While these platforms can enhance transparency in certain aspects of scientific production, our research also highlights systematic biases that shape which findings receive the greatest visibility (PNAS, 2021; JCMC, 2024; Nature Communications, 2025).
Our recent work addresses emerging challenges associated with large language models (LLMs) and shifts in linguistic style (Science Advances, 2025). In parallel, we have examined the lifecycle of retracted scientific articles, those withdrawn due to error or misconduct, and traced how such findings continue to circulate across digital platforms (PNAS, 2022; PUS, 2024). Knowledge repositories such as Wikipedia may continue to reference retracted research and serve as a key resource for both public audiences and the training of subsequent LLMs (CSCW, 2026).
We have also conducted survey-based research exploring the tension between scientific rigor and the incentives for virality in digital contexts (CI, 2025b; OSF, 2026). This tension plays a critical role in shaping how scholars engage with digital platforms when communicating their findings, often requiring trade-offs between thoroughness and immediate impact.
Collectively, this line of work informs both research policy and the design of digital platforms.
