I am an Assistant Professor in the Department of Communication Science at the Vrije Universiteit Amsterdam and a Faculty Associate in the Center for Social Media and Politics at New York University. I received my PhD in Political Science from the University of Washington . Before joining VU Amsterdam, I was a Moore Sloan research fellow in the Center for Data Science and Center for Social Media and Politics at the New York University; and a research fellow at the Amsterdam School of Communication Research.
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.
Abstract: Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.
Abstract: A narrow information diet may be partly to blame for the growing political divides in the United States, suggesting exposure to dissimilar views as a remedy. These efforts, however, could be counterproductive, exacerbating attitude and affective polarization. Yet findings on whether such boomerang effect exists are mixed and the consequences of dissimilar exposure on other important outcomes remain unexplored. To contribute to this debate, we rely on a preregistered longitudinal experimental design combining participants' survey self-reports and their behavioral browsing data, in which one should observe boomerang effects. We incentivized liberals to read political articles on extreme conservative outlets (Breitbart, The American Spectator, and The Blaze) and conservatives to read extreme left-leaning sites (Mother Jones, Democracy Now, and The Nation). We maximize ecological validity by embedding the treatment in a larger project that tracks over time changes in online exposure and attitudes. We explored the effects on attitude and affective polarization, as well as on perceptions of the political system, support for democratic principles, and personal well-being. Overall we find little evidence of boomerang effects.
Abstract: This study investigates the potential role both untrustworthy and partisan websites play in misinforming audiences by testing whether actual exposure to these sites is associated with political misperceptions. Using a sample of American adult social media users, we match data from individuals’ Internet browser histories with a survey measuring the accuracy of political beliefs. We find that visits to partisan websites are at times related to misperceptions consistent with the political bias of the site. However, we do not find strong evidence that untrustworthy websites consistently relate to false beliefs. There is also little evidence that visits to less partisan, centrist news sites are associated with more accurate political beliefs about these issues, suggesting that exposure to politically neutral news is not necessarily the antidote to misinformation. Results suggest that focusing on partisan news sites—rather than untrustworthy sites—may be fruitful to understanding how media contribute to political misperceptions.
Abstract: Although artificial intelligence is blamed for many societal challenges, it also has underexplored potential in political contexts online. We rely on six preregistered experiments in three countries (N = 6,728) to test the expectation that AI and AI-assisted humans would be perceived more favorably than humans (a) across various content moderation, generation, and recommendation scenarios and (b) when exposing individuals to counter-attitudinal political information. Contrary to the preregistered hypotheses, participants see human agents as more just than AI across the scenarios tested, with the exception of news recommendations. At the same time, participants are not more open to counter-attitudinal information attributed to AI rather than a human or an AI-assisted human. These findings, which—with minor variations—emerged across countries, scenarios, and issues, suggest that human intervention is preferred online and that people reject dissimilar information regardless of its source. We discuss the theoretical and practical implications of these findings.
Abstract: Affective polarization is a key concern in America and other democracies. Although past evidence suggests some ways to minimize it, there are no easily applicable interventions that have been found to work in the increasingly polarized climate. This project examines whether irrelevant factors, or incidental happiness more specifically, have the power to reduce affective polarization (i.e., misattribution of affect or “carryover effect”). On the flip side, happiness can minimize systematic processing, thus enhancing beliefs in conspiracy theories and impeding individual ability to recognize deep fakes. Three preregistered survey experiments in the US, Poland, and the Netherlands (total N = 3611) induced happiness in three distinct ways. Happiness had no effects on affective polarization toward political outgroups and hostility toward various divisive social groups, and also on endorsement of conspiracy theories and beliefs that a deep fake was real. Two additional studies in the US and Poland (total N = 2220), also induced anger and anxiety, confirming that all these incidental emotions had null effects. These findings, which emerged uniformly in three different countries, among different partisan and ideological groups, and for those for whom the inductions were differently effective, underscore the stability of outgroup attitudes in contemporary America and other countries.
Abstract: Agenda setting and issue framing research investigates how frames impact public attention, policy decisions, and political outcomes. Social media sites, such as Twitter, provide opportunities to study framing dynamics in an important area of political discourse. We present a method for identifying frames in tweets and measuring their effectiveness. We use topic modeling combined with manual validation to identify recurrent problem frames and topics in thousands of tweets by gun rights and gun control groups following the Marjory Stoneman Douglas High School in Parkland, Florida, shooting. We find that each side used Twitter to advance policy narratives about the problem in Parkland. Gun rights groups’ narratives implied that more gun restrictions were not the solution. Their most effective frame focused on officials’ failures to enforce existing laws. In contrast, gun control groups portrayed easy access to guns as the problem and emphasized the importance of mobilizing politically to force change.
Abstract: 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.
Abstract: 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.
Abstract: Do images affect online 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 online participation in social movements. We first confirm that images have a positive mobilizing effect in the context of online protest activity. We then argue that images are mobilizing because they trigger stronger emotional reactions than text. Building on existing political psychology models we theorize that images evoking enthusiasm, anger, and fear should be particularly mobilizing, while sadness should be demobilizing. We test the argument through a study of Twitter activity related to a Black Lives Matter protest. We find that both images in general and some of the proposed emotional attributes (enthusiasm and fear) contribute to online participation. The results hold when controlling for alternative theoretical mechanisms for why images should be mobilizing, as well as for the presence of frequent image features. Our paper thus provides evidence supporting the broad argument that images increase the likelihood of a protest to spread online while also teasing out the mechanisms at play in a new media environment.
Abstract: Foundations are much more than disinterested philanthropic institutions that award grants to service-providing nonprofits. Foundations are political actors that seek to produce social change, not only by donating resources to nonprofits that promote causes but also by supporting policy reform in a more direct manner. We investigate engagement in advocacy among community foundations in the United States, which we define as the effort to influence public policy by proposing or endorsing ideas and by mobilizing stakeholders for social change. Drawing primarily on organizational sociology, we posit that the environmental context in which community foundations are situated and particular structural characteristics or operational features of community foundations (institutional logics, identity and embeddedness, and managerialism) will be associated with advocacy. We utilize machine learning techniques to establish an outcome measure of advocacy discourse on community foundation websites and ordinary least squares (OLS) regression to model that outcome with a cross-sectional dataset compiled from multiple sources. We find considerable support for our conceptual frame, and we conclude by offering an agenda for future research on foundations as interest groups.
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.
The protest mobilization literature has long suggested that social ties influence a person’s decision to protest. However, previous research has not clearly established why this is the case. We consider four possible mechanisms: networks mobilize because they a) provide logistical information, b) motivate people about the cause, c) solve coordination problems, and d) apply social pressure to attend. We test these with novel protest attendance data. We identify Twitter users who participated in the 2018 U.S. Women’s March, categorize the tweets they could have seen, and compare these and their network positions to those of individuals who were similar but did not participate. We find strong evidence that networks indeed mattered for mobilization. Furthermore, we show that even after controlling for a variety of confounders, exposure to messages providing information, helping to coordinate, and applying pressure predicted protest attendance while exposure to messages seeking to motivate did not.
A rapidly growing body of research in political science uses unsupervised topic modeling techniques such as Latent Dirichlet Allocation (LDA) models to construct measures of topic attention for the purpose of hypothesis testing. A central advantage of unsupervised topic modeling, compared to supervised approaches, is that it does not require advance knowledge of the topics to be studied, or a sizable set of training examples. However, the topics discovered using these methods can be unstable. This is potentially problematic to the extent that researchers report results based on a single topic model specification. We propose an approach to using topic model results for hypothesis testing that incorporates information from multiple specifications. We then illustrate this robust approach by replicating an influential political science study. An R package (ldaRobust) for its implementation is provided.
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.