Computational Social Science Data for Good Seminar: The Analysis of News Consumption through Co-Exposure Networks
Location:
Event Type:
The Analysis of News Consumption through Co-Exposure Networks
Friday, March 8, 1-2:30pm
ISERP Conference Room (IAB 270B)
Lunch provided | RSVP required
Online platforms are becoming the main source of news for the majority of the population, and yet there is still a lack of comparative evidence analyzing patterns of news consumption across political contexts. In this talk, I will discuss an ongoing project that employs network science tools to uncover aggregate patterns of news consumption and determine whether there is evidence of fragmentation and self-selection across demographic groups and national contexts. The core of the approach consists on analyzing exposure networks where the nodes are news sources and the edges map the number of users co-exposed to those sources. The analysis of these networks allows us to build standardized indicators of exposure to news that we can then compare across countries, demographic groups, and digital platforms – thus offering evidence to test ongoing claims of the effects of digital technologies on access to political information.
Speaker Bio:
Sandra González-Bailón's research areas include network science, data mining, computational tools, and political communication. Her applied research focuses on the analysis of social media, political protests, mobilization dynamics, information diffusion, and news consumption.
Sandra González-Bailón is an Associate Professor at the Annenberg School for Communication, and affiliated faculty at the Warren Center for Network and Data Sciences(link is external). Prior to joining Penn, she was a Research Fellow at the Oxford Internet Institute(link is external) (2008-2013), where she is now a Research Associate(link is external). She completed her doctoral degree in Nuffield College(link is external) (University of Oxford) and her undergraduate studies at the University of Barcelona(link is external). Her research lies at the intersection of network science, data mining, computational tools, and political communication. She leads the research group DiMeNet –acronym for Digital Media, Networks, and Political Communication.
Her book Decoding the Social World(link is external) (MIT Press, December 2017) explains how data science and the analysis of networks help us solve the puzzle of unintended consequences – or why our intentional actions often trigger outcomes that we did not intend or even envision. The key to the puzzle, the book argues, is to integrate different levels of analysis in our theories of social change, something we can do now because of new data and computational tools.
Her most recent project, “Digital News and the Consumption of Information Online” (2017-2020) is funded by the National Science Foundation.