I am a computational social scientist, broadly interested in studying news consumption as a socio-political process. To gain a holistic understanding of how it works, I situate my doctoral research around two distinct yet complementary approaches.
The first approach tries to understand news consumption at a macro-level. In a paper that we published earlier, we introduced a network science-based method for analyzing news consumption patterns that allowed for statistically robust cross-national comparisons. For the purposes of my dissertation, I extend this method to the context of India and build on it to answer more substantive and relevant questions that I then use to advance a more general theoretical framework for understanding how audiences operate across different countries. How, for instance, does the unique spatio-linguistic divide in India correlate with the community-wise fragmentation of the observed news consumption network? Which actors are primarily responsible for driving this fragmentation? How does the extent of network fragmentation change over time and respond to political events in the country?
The second approach that I espouse complements this top-down approach by trying to understand human agency in the news consumption process. I’m currently running large-scale web experiments to understand why people make the choices they make while consuming news online, and the effects that partisan news exposure have in exacerbating the existing political divides in American society.
Subhayan Mukerjee is a fourth year doctoral candidate at the Annenberg School for Communication.