Research
I study how attention converges, how intense moments reorder political visibility, and how AI alters the people, systems, and messages competing for attention.
Media storms are explosive increases in attention to a single story. My research develops computational methods to identify them across decades of news and explains why storms take different temporal forms—from explosive outbursts to slow boils and dominant plateaus.
Periods of disrupted gatekeeping may open windows of opportunity for less-established political actors. I examine which actors gain exceptional visibility during storms, when they enter the story, and whether that visibility persists.
How does the composition of AI research teams shape engagement with ethical and societal concerns? This work maps a decade of AI scholarship to study interdisciplinarity, corporate collaboration, and whose priorities define responsible AI.
I am developing research on artificial sourcehood, receiver-contingent AI persuasion, and the validity of using large language models to simulate participants in social-science experiments.
Methodological approach
My work is defined by operationalizing amorphous theoretical concepts at scale. I use natural language processing, time-series analysis, experiments, and human-in-the-loop research designs to test and refine communication theory.