I am a Moore Sloan research fellow in the Center for Data Science at the New York University, where I work in the Social Media and Political Participation lab. I am also a PhD candidate in the Department of Political Science at the University of Washington.
My research interests encompass the areas of political communication, public policy processes, 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 natural language processing (text as data), computer vision (images as data), and machine learning and artificial intelligence in general.
In my dissertation I study the conditions under which social media communications allow civil society organizations such as unions and citizen groups to solve engagement problems. In other published and ongoing work I also focus on how social media has shaped collective action dynamics for social movements and advocacy groups. In a NSF-funded project with John Wilkerson and Matt Denny we develop text as data methods to study the lawmaking process in the U.S. Congress; and in another NSF-funded project with John Wilkerson and Nora Webb Williams we develop computer vision methods to study how images shared in social media contribute to the diffusion of outsider groups such as civil society organizations, social movements, and radical violent groups.
Prior to 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.
For more than half a century, scholars have been studying legislative effectiveness using a single metric - whether the bills a member sponsors progress through the legislative process. We investigate a less orthodox form of effectiveness - bill proposals that become law as provisions of other bills. Counting these “hitchhiker” bills as additional cases of bill sponsorship success reveals a more productive, less hierarchical and less partisan lawmaking process. We argue that agenda and procedural constraints are central to understanding why lawmakers pursue hitchhiker strategies. We also investigate the legislative vehicles that attract hitchhikers and find, among other things, that more Senate bills are enacted as hitchhikers on House laws than become law on their own.
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.
Are legislators responsive to the issue demands of the public? If so, are they more likely to be responsive to some citizens than to others? Research on agenda setting and responsiveness finds a correspondence between the issue priorities of the public and politicians, but it does not provide conclusive evidence on who influences whom. We argue that determining the direction of the effect is of great relevance to adjudicate between particular models of representation and to judge political responsiveness generally. We fill this current gap by studying all tweets sent by Members of the United States Congress, sets of Democratic and Republican supporters and attentive citizens, and a random sample of Twitter users located in the United States from January 2013 to December 2014. Using a Latent Dirichlet Allocation model, we extract topics that represent the diversity of issues that legislators discuss. Then, we exploit variation in the distribution of topics over time to test whether Members of Congress lead or follow their constituents in their selection of issues to discuss. We find that legislators are more likely to respond to than to influence discussion of public issues, results that hold even after controlling for media effects. We also find that legislators are more likely to be responsive to their own supporters and attentive voters than to the general public.
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