I am a PhD student in the Department of Political Science at the University of Washington. I am also a La Caixa Fellow and a research fellow at the Center for American Politics and Public Policy.
My research interests encompass the areas of political communication, comparative public policy, and computational social sciences. I am particularly interested in how social movements and interest groups influence the political agenda and the decision making process in the current media environment. My methodological interests and strengths are causal inference, computer vision, natural language processing, and machine learning and artificial intelligence in general. In my dissertation I use computer vision methods to study under which circumstances online visual communications help advocacy groups get their message across. I also work with my advisor John Wilkerson on a NSF-funded big data project studying how the content of bills evolve as they move through the legislative process.
Prior to starting graduate school, I received a B.A. in political science from the University of Barcelona and I worked as a research assistant for the Spanish Policy Agendas Project. Outside the academic sphere, I love all kinds of sports and music. I played cello for many years and I like listening to Schoenberg and Kodaly. I specially love Starker's recording of Kodaly's cello solo.
Abstract: Strong party brands help congressional parties elect candidates, maintain or gain majority control, and advance their policy agendas. Because successful branding efforts depend on consistent messaging, party leaders try to choose issues that most members are willing to promote. But what do leaders do when a party majority pressures them to take up issues that harm the brand for others? We investigate the 2013 government shutdown as a branding event. House Republican leaders instigated the shutdown after learning that a majority of Republicans would not vote for a clean funding bill. However, instead of highlighting the issues that led to the shutdown, they publicized the party's efforts to resolve it. Party leaders sought to exploit the fact that party brands have both position and valence components to simultaneously address the demands of the party base and the electoral concerns of members representing competitive districts.
Abstract: Text has always been an important data source in political science. What has changed in recent years is the feasibility of investigating large amounts of text quantitatively. The internet provides political scientists with more data than their mentors could have imagined, and the research community is providing accessible text analysis software packages, along with training and support. As a result, text as data research is beginning to mainstream in political science. Scholars are tapping new data sources, they are employing more diverse methods, and they are becoming critical consumers of findings based on those methods. In this article, we first introduce readers to the subject by describing the four stages of a typical text as data project. We then review recent political science applications, and explore one important methodological challenge - topic model instability - in greater detail.
Abstract: In May 2011, thousands of outraged citizens (i.e. the indignados) occupied the squares of the main Spanish cities to express their discontent and claim for reforms. This article investigates via Twitter messages the ability of the 15-M movement to place their claims into the media agenda and to keep ownership of their own discourse. The analysis emphasizes the fact that the social movement originated in the Internet with a highly decentralized structure and with scarce organizational resources. Results show that protesters discourse included a great number of claims, although the activists focused their discussions on three specific issues: electoral and party systems, democracy and governance, and finally, civil liberties. Moreover, the study reveals that the indignados managed to keep control over their repertoires and were able to determine the media agenda despite the later mainly focused on the most dramatic events.
Do images affect political mobilization? If so, how? These questions are of fundamental importance to scholars of social movements, contentious politics, and political behavior generally. However, little prior work has systematically addressed the role of images in mobilizing participation in social movements. We theorize that images are more easily processed than text, lowering the cost of deciding to participate in a social movement. In addition, images might trigger emotional responses, increase expectations of success, and generate collective identity; all leading to greater mobilization. We test these theories though a study of Black Lives Matter, utilizing both observational and experimental data. We find that both images in general and the proposed key attributes of images contribute to online participation. Our paper thus provides evidence supporting the broad argument that images increase the likelihood of a protest to spread while also teasing out the mechanisms at play in a new media environment.
We investigate lawmaking by examining bill markup patterns in Congress. How much do bills change as they move through the legislative process? Where does most bill editing occur? Does more editing improve a bill’s prospects? Why are some bills edited more than others? Which chamber has more influence over the final substance of laws? How does institutional context, such as divided government, impact the bill markup process? The first part of this paper describes some challenges of reliably comparing the substance of bill versions, and validates an automated approach to measuring how much editing occurs between different stages of the legislative process. We then begin to examine lawmaking patterns in Congress. Our results begin to answer many longstanding questions. For example, researchers have noted that most of the bills that become law are sponsored by members of the House of Representatives. We find that the final substance of these bills tends to be more similar to what the Senate proposes. Overall our results demonstrate the feasibility and value of using bill markup to study legislative institutions and behavior.
Social scientists have long argued that images play a crucial role in politics. This role is heightened by the bombardment of images that people experience today. Digitization has both increased the presence of images in daily life and made it easier for scholars to access and collect large quantities of pictures and videos. However, using images as data for social science inference is an arduous task. Political scientists have therefore often turned to other data sources and puzzles, leaving substantive theoretical questions unanswered. Fortunately, recent innovations in computer vision can reduce the costs of using images as data. The goals of this project are twofold. First, we build on existing computer vision methods to present a set of automatic techniques that will aid political scientists working with images. We highlight the potential of Convolutional Neural Nets for automatic object detection and recognition; for face detection and recognition; and for visual sentiment analysis. Second, we apply these techniques to a novel dataset of Black Lives Matter Twitter protest images, demonstrating the ability of computer vision methods to replicate gold standard manual image labels.
Political scientists frequently study the public communications of members of Congress to better understand their electoral strategies, policy responsiveness, ability to influence public opinion and media coverage of Congress. However, different studies base their conclusions on different communication channels, including (among others) member websites, newsletters, press releases, and social media. All these scholars have taken these individual sources as fully representative of the communication strategy of the elected official as a whole. However, what has not been asked is whether members communicate the same or different messages across these differing channels? In this paper we look at the member press releases, Twitter, and Facebook messages sent from August to December 2014 in order to study to what extent their communication strategy is consistent across channels. We use an automatic semi-supervised method to classify the messages into political issues and to assess the validity of inferring broader communication patterns from a single source.
These are courses I have taught as a Teacher Assistant at the University of Washington:
A python module that provides a set of functions to fit multiple LDA models to a text corpus and then search for the robust topics present in multiple models
Replication material for the paper by John Wilkerson and Andreu Casas on Text as Data at the Annual Review of Political Science