RESEARCH
"The Impact of Political Campaigns on Demand for Partisan News"
Abstract
How do individuals acquire political information during election campaigns? This paper examines the demand for political news using a unique dataset that combines audience viewership data with the full text of news stories from Spanish TV channels. I estimate a structural random coefficients demand model to capture heterogeneity in political preferences, incorporating demographic differences. A key challenge in mea- suring demand for political content is the endogeneity of news supply. To address this, I propose a novel identification strategy that exploits exogenous variation in the daily news landscape. By classifying all stories produced in Spain on a given day, I construct a set of potential inputs available to TV channels. Since channels have estab- lished political stances and face short-run costs in deviating from them, fluctuations in the political composition of available stories constrain left- and right-leaning outlets asymmetrically. Using Large Language Models, I categorize the tone of each story with respect to political parties. My findings show that political campaigns significantly al- ter news consumption patterns. While there is no clear partisan asymmetry in demand before campaigns, polarization emerges once the campaign begins. Right-leaning view- ers increasingly seek positive coverage of their own party and negative coverage of the opposition. These results highlight the role of campaign dynamics in shaping media consumption and contribute to the broader literature on political media bias and voter behavior.
"Breaking the Echo Chamber: Nonviolent Protest and Police Violence on Twitter"
with Hannes Mueller, Daniel Montolio, and Francesco Slataper.
Abstract
Social media platforms play a crucial role in shaping public discourse and political con- flict, but social media landscape is shaped in turn by the unfolding of the conflict. This paper incorporates this dynamic reciprocity in a microfounded model of online inter- actions, built upon a granular analysis of how social media users responded to events around Catalonia’s independence referendum in 2017. We analyze retweets within and across Catalan and Spanish language groups using linguistic markers from a corpus of 26 million tweets. We define two channels to explain change in retweet behavior: ex- posure—the likelihood of encountering specific content— and retweet rates—the prob- ability of retweeting content once exposed. Remarkably, we can document a dramatic increase in retweet rates between language groups and greater exposure to cross-group content. We contrast this response with the reaction to a terror attack in 2017 and explore the role of political ideology in our findings.
"Online and Offline protest participation: An Empirical Analysis for the 2020 Black Lives Matter Movement"
Abstract
The recent wave of world-wide protests that took place after George Floyd’s killing has sparked attention in the Black Lives Matter movement, specially in terms of online activism. How does offline protesting behavior interact with the underlying online social networks? In this work, I build a classification algorithm to identify individuals who physically participated in the BLM demonstrations across the US. Thanks to this unique dataset, I explore at an individual level their full Twitter activity to better understand the role of influential users, coordination patterns and speech evolution. Through this analysis, I aim to examine assumptions regarding slacktivism, which involves engaging in online activism with minimal effort, in comparison to more traditional forms of protesting. By exploring how social media contributes to the development of traditional activism, we can gain a deeper understanding of the role of social media in protesting behavior.
