Ernesto Calvo, Ph.D.

I am the Director of the Interdisciplinary Lab for Computational Social Science (iLCSS) and a Professor of Government and Politics at the University of Maryland. I research comparative political institutions, social media, political representation, and social networks. My work lies at the intersection of big data, survey experiments, and institutions.

I am the author of a number of books on comparative institutions and social media, including Non-Policy Politics: Rich Voters, Poor Voters, and the Diversification of Electoral Strategies (Cambridge University Press 2019) with María Victoria Murillo; Legislator Success in Fragmented Congresses in Argentina (Cambridge University Press 2014); and Fake News, Burbujas, Trolls y Otros Encantos: Cómo funcionan (para bien y para mal) las redes sociales (Siglo XXI Editores 2020) with Natalia Aruguete. I have authored over 70 publications in the United States, Latin America and the Caribbean, and Europe. My research has been recognized by the American Political Science Association with the Lawrence Longley Award, the Luebbert Best Article Award, and the Michael Wallerstein Award.

View my CV Here.

Ernesto Calvo

Featured Books

Featured Research

Winning! Electoral Adjudication and Dialogue in Social Media

This article introduces the concept of adjudication to define the act of granting or denying ownership of an outcome to individuals or groups in social media. We extend existing models of political dialogue to explain differences between winners and losers on social medial when elections are adjudicated. We use Twitter data on four elections in Argentina (2019), Brazil (2018), United Kingdom (2019), and the United States (2016). Our findings show an increase in event salience upon adjudication, followed by a more extensive dialogue among winners and disen- gagement among losers. Further, we show differences in the network structure of dialogue, with dialogue in winning communities displaying a wider periphery and dialogue in losing communities being more hierarchical and more uncivil. We identify the causal effects of adjudication using a regression discontinuity design.
Research

Ernesto Calvo

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News Sharing, Gatekeeping, and Polarization: A study of the #Bolsonaro Election

The increasing importance of news sharing, and its effect on the routines, practices, and values described by the hierarchy of influences model, raises new questions about content cre- ation in polarized social media environments. How does news sharing change the gatekeeping preferences of news organizations? Will polarized users polarize news organizations further? In this article, we model news sharing behavior in social media and derive implications for the study of gatekeeping in political communication. We model users’ news sharing behavior using observational data from Twitter and then use the parameters from our model to explain its effect on editorial gatekeeping. The article provides a road map for researchers interested in the relationship between these major theories in political communication. We test our model using Twitter data collected during the election of populist leader Jair Bolsonaro in Brazil.
Research

Ernesto Calvo, Natalia Aruguete, and Tiago Ventura

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Will I get COVID-19? Partisanship, Social Media Frames, and Perceptions of Health Risk in Brazil Brazil.

In these polarized and challenging times, not even perceptions of personal risk are immune to partisanship. In this paper, we report results from a new survey with an embedded social media experiment conducted during the first months of the COVID-19 pandemic in Brazil. Descriptive results show that pro-government and opposition partisans report very different expectations of health and job risks. Job and health policy have become wedge issues that elicit partisan responses. We exploit random variation in the survey recruitment to show the effects of the first President’s speech on national TV on risk perceptions and how partisanship moderates these results. We conclude with a framing experiment that models key cognitive mechanisms driving partisan differences in perceptions of health risks and job security during the COVID-19 crisis.
Research

Ernesto Calvo, Tiago Ventura

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