RESEARCH
Job Market Paper
"The Impact of Political Campaigns on Demand for Partisan News"
Abstract
Political polarization in news consumption has recently gained attention, yet policies to limit it are hard to evaluate. This paper introduces a novel, self-collected dataset on Spanish prime-time TV news. I identify stories minute-by-minute and match them across newscasts to compare editorial treatment. Combining machine-learning methods with large language models, I classify the partisan slant of each story and use the resulting data to document changes in news coverage during the 2023 presidential campaign. I then match these data to high-frequency audience-meter records and estimate a random-coefficients demand model, using shifts in the daily wire-service story mix as instruments for slant. I find significant evidence of affective polarization only after the election campaign begins. Given the demand estimates, I back out outlets’ content preferences from a horizontal-differentiation game. Finally, I run counterfactual simulations to assess the effect of policies regulating campaign airtime. My framework offers a tractable tool for evaluating media policies aimed at content fairness or fighting misinformation.
Other Works
"Breaking the Echo Chamber: Nonviolent Protest and Police Violence on Twitter"
with Hannes Mueller, Daniel Montolio, and Francesco Slataper.
Abstract
This article exploits data from a political conflict between language groups to show how political events can rapidly redefine how these groups interact on social media. Leveraging on a unique dataset of 26 million retweets by 120 000 Catalan- and Spanish-speaking Twitter users, we estimate individual exposure to tweets with a network-based model. We then compare two shocks in the same region and year: the Barcelona terror attack and the Catalan independence referendum. The referendum — and the circulated images of police violence — triggered a sharp, symmetric jump in cross-language retweeting. The terror attack, by contrast, did not lead to a similar realignment.
"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.
"Media Entry and Political Slant"
with Manuel Lleonart.
Abstract
his paper investigates how competition shapes ideological slant in television news. While theoretical models suggest that increased media competition can either intensify or mitigate bias—depending on whether audiences seek confirmation or accuracy—empirical evidence remains limited. We address this gap by analyzing the entry of a new Spanish TV news outlet and measuring how it alters the political slant of existing providers. Our approach combines a formal model of political media markets with a novel empirical strategy that leverages large-language-model and text-analysis techniques. We disentangle media bias into topic selection, ideological tone, and airtime allocation—capturing the three primary channels through which slant manifests. Our findings offer the first direct evidence of how heightened rivalry influences not just audience composition, but the strategic editorial decisions that shape political coverage.
PROJECTS
Spanish Media Monitor
Spanish Media Monitor is the first effort to monitor TV media using large language models (LLMs). I built this project to provide real-time analysis of Spanish television news content, leveraging LLMs for story identification, classification, and visualization. You can visit the webpage here.
TEACHING
Instructor:
Teaching Assistant:
Course evaluations available upon request.
