2019-2020 Fall

Jon Krosnick, Department of Communication at Stanford University 

September 23, 2019

Prevalence and Moderators of the Candidate Name-Order Effect: Evidence from Statewide General Elections in California

ABSTRACTAlthough some past studies suggest that candidates may receive more votes when their names are listed first among their competitors than when they are listed later, two recent studies challenged this conclusion with regard to major-party candidates running in statewide races and raised questions about the impact of analytic methods on the conclusions of name-order research. Using the largest data set to date— a set of quasi-randomized natural experiments involving 402 candidates running in 76 statewide California elections—this study tests a series of hypotheses about the conditions under which name-order effects are most likely. Regardless of the analytic method used, a small primacy effect appeared consistently that could have a substantive impact on some contests. This effect was larger in races for lower-visibility offices, in years with higher turnout, and in races that were not close. All of this is consistent with the claim that name-order effects occur among voters who have little or no information about the candidates or among voters who feel ambivalence about the candidates. 

Lisanne Wichgers, Department of Communication at the University and Amsterdam 

September 30, 2019

The societal and behavioral effects of hate speech prosecution of anti-immigration politicians

ABSTRACTThe rise of anti-immigration parties in Europe has been accompanied by controversy; several anti-immigration politicians have faced prosecution for inciting hatred. The main question of my dissertation is: what are the societal and behavioral consequences of hate speech prosecution of anti-immigration politicians? Prior research demonstrated that legal measures affect voting behavior, but the effects likely go beyond voting behavior. In my first dissertation study, I have therefore investigated the effects of hate speech prosecution on citizens’ democratic support, by using experimental methods. In a follow-up study I will compare different contexts (the Netherlands versus the United States) and will include different target groups. Additionally, I have used the hate speech prosecution case of the Dutch politician Geert Wilders to investigate which non-media actors are successful in frame-building of political issues and why, and to what extent there is a reciprocal influence between non-media actors and journalists in frame-building. 

Lisanne Wichgers

Luzia Helfer, Geneva University of Switzerland, Stanford University Fulbright Visiting Postdoctoral Scholar

October 14, 2019

Social status, inequality perceptions and support for redistributive policies among politicians

ABSTRACT: Economic inequality constitutes a defining challenge of our time and it remains puzzling to scholars why rising levels of inequality have not lead to more redistributive policies. To better understand the missing response in the political system, a focus on politicians and their perceptions of inequality is warranted. Building on recent advances in the literature, we see support for redistributive policies as dependent on the individual social status. In a first study, we seek to understand the mechanisms connecting a legislators’ socio-economic status, their perceptions of inequalities and support for redistributive policies. We draw on data from Swiss national legislators (N=108) and a representative survey of Swiss citizens (N=4537). Building on these findings, our second study zooms in on the causal mechanisms. In a survey experiment with Swiss regional legislators (N=137), we manipulate politicians’ perceptions of the socio-economic hierarchy and their place in it to study the effects of the social distance on a range of redistributive policy preferences. Our findings show that legislators tend to perceive inequalities as more fair than citizens and that ideology plays a key role in explaining their perceptions of inequalities and support for redistribution.

Luzia Helfer

David Yeager, Department of Psychology at University of Texas at Austin

James Pustejovsky, Department of Educational Psychology at University of Texas at Austin

October 21, 2019

The Impossible Mediation Test: A Method for Dealing with Plausibly Confounded Mediation

ABSTRACT: In much psychology research, mediators are measured, not manipulated. Therefore, the paths from mediators to outcomes—the so-called b paths—can be confounded by omitted variable bias, as in any other correlational analysis. The present research builds on the logic of falsification tests in econometrics and sensitivity analysis in statistics to propose the impossible mediation test, which can quantify the amount of confounded mediation. Researchers can add to an experiment a condition in which the dependent variable is measured first, then the manipulation is implemented, and finally, the posited mediator is measured. This allows for assessment of the spurious association between the dependent variable and the mediator, and statistics can be estimated as if the dependent variable was measured after the manipulation was implemented, to assess whether the spurious association is sufficiently strong to yield the false appearance of mediation. This estimate of “impossible” mediation can be compared to the results obtained from data where the dependent variable is actually measured in the conventional order after the mediator, to determine whether evidence of mediation is stronger in the latter case than the former. Evidence of mediation that survives the impossible mediation test constitutes a strong basis for a claim about mediation of a causal process. The talk illustrates this procedure with an empirical example.

James Pustejovsky

Eran Amsalem, Department of Communication at the Hebrew University of Jerusalem and the Department of Political Science at the University of Antwerp, visiting the Department of Communication at Stanford University

October 28, 2019

Framing Effects on Citizens’ Attitudes, Emotions, and Behavior: A Meta-Analysis


Even though framing is one of the most widely used concepts for studying elite and media influence, there remains significant disagreement in the literature regarding its overall efficacy in swaying citizens. Over the years, framing effects have been described, alternatively, as large and meaningful; real but conditional in nature; limited and minimal; and even irrelevant. The goal of this study is to assess these conflicting claims empirically by systematically meta-analyzing the large and diverse literature on framing effects in the political domain. A combined analysis of 135 experiments from over three decades (N = 57,877) reveals that when examined across contexts, framing exerts medium-sized effects on citizens’ political attitudes (d = .42) and their emotional reactions (d = .47). However, when it comes to what matters most for the political process—affecting citizens’ behavior—framing effects are much smaller (d = .12). Similar small average effects are found in studies simulating real-world political scenarios by employing frame competition (d = .18). Overall, our findings suggest that although framing affects citizens, its influence tends to be more limited in the real world.

Eran Amsalem

Munmun DeChoudhury, Professor at Georgia Tech’s School of Interactive Computing

November 4, 2019

Employing Social Media to Improve Mental Health: Harnessing the Potentials and Avoiding the Pitfalls


Social media data is being increasingly advocated to computationally learn about and infer the mental health states of individuals and populations. Despite being touted as a powerful means to shape interventions and impact mental health recovery, little do we understand about the theoretical, domain, and psychometric validity of this novel information source, or its underlying biases, when appropriated to augment conventionally gathered data, such as surveys and verbal self-reports. This talk presents a critical analytic perspective on the pitfalls of social media signals of mental health, especially when they are derived from “proxy” diagnostic indicators, often removed from the real-world context in which they are likely to be used. Then, to overcome these pitfalls, this talk presents results from two case studies, where computational algorithms to glean mental health insights from social media were developed in a context-centered way, in collaboration with domain experts and stakeholders. The first of these case studies, a collaboration with Northwell Health, focuses on the individual-perspective, and reveals the ability and implications of using social media data of consented schizophrenia patients to forecast relapse and support clinical decision-making. Scaling up to populations, in collaboration with the Centers for Disease Control and Prevention and towards influencing public health policy, the second case study seeks to forecast nationwide rates of suicide fatalities using social media signals, in conjunction with health services data. The talk concludes with discussions of the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda, that incorporates methodological rigor, ethics, and accountability.

Munmun DeChoudhury

Lisa Henriksen, Stanford Prevention Research Center

November 11, 2019

Advancing Science & Policy in the Retail Environment for Tobacco


A 78% increase in past-month vaping among US high-school students from 2017 to 2018 was the largest recorded increase for any substance use in a half-century. The retail environment contributes to tobacco use/disparities in multiple ways. Goals for this seminar are to: (1) synthesize two decades of research from my lab group with relevance for communication researchers and (2) engage your feedback about two or three on-going studies that would benefit from improving measures of opinions about tobacco products and tobacco control policies.

Lisa Henriksen

Hakeem Jefferson, Professor of Political Science at Stanford University

November 18, 2019

The Curious Case of Black Conservatives: Construct Validity and the 7-point LiberalConservative Scale


Scholars have long puzzled over the existence of conservative-identifying black Americans who nonetheless identify with and vote for the Democratic Party. This paper resolves this paradox. Leveraging data from the American National Election Study, I demonstrate that the terms “liberal” and “conservative” are unfamiliar to many black Americans, rendering the commonly used 7-point liberal-conservative measure of ideology invalid for this population. Black respondents unfamiliar with these terms misapply them and choose ideological labels that fail to reflect their partisan preferences. Consequently, scholars and political actors make incorrect and imprecise inferences about the contours of black politics in the United States. Moreover, this article raises new concerns about the generalizability of claims that rely on ideological self-identification measures, including popular claims about polarization among the mass public. This work also suggests a need for caution when using concepts that vary in their meaningfulness across social groups.

Hakeem Jefferson

Darren Gergle, Professor of Communication Studies at Northwestern University

December 2, 2019

The Effect of Peer-Production Biases on Community-Maintained Information Repositories


Every day people produce an extraordinary amount of user-generated content on peerproduction and social media platforms such as Wikipedia, OpenStreetMap, Reddit, Instagram and Facebook. These large-scale repositories have been shown to contain wide-ranging coverage of a variety of events, topics and information. Yet, who contributes, what they contribute and how they integrate it, can undermine the subsequent availability, accessibility and usefulness of the information.

In this talk, I will present research that illustrates both the challenges and opportunities of user- generated content in the context of multiple language editions of Wikipedia and OpenStreetMap. In doing so, I aim to achieve the following three goals: (1) to describe large-scale data analysis techniques that can be used to empirically assess content diversity and important biases that exist, (2) to elaborate the effect these diverse representations and biases can have on both individuals that make use of the knowledge as well as technologies that rely upon peer-produced data structures, and (3) to discuss new approaches that leverage diversity in positive ways to support more global representation.

Darren Gergle