The consequences of online partisan media.
Proceedings of the National Academy of Sciences
Co-authored with Andrew M. Guess, Simon Munzert, and JungHwan Yang
Link | Supplementary Materials | Replication code and data | Pre-analysis plan | Expand abstract »
What role do ideologically extreme media play in the polarization of society? Here we report results from a randomized longitudinal field experiment embedded in a nationally representative online panel survey (N = 1,037) in which participants were incentivized to change their browser default settings and social media following patterns, boosting the likelihood of encountering news with either a left-leaning (HuffPost) or right-leaning (Fox News) slant during the 2018 US midterm election campaign. Data on ≈ 19 million web visits by respondents indicate that resulting changes in news consumption persisted for at least 8 wk. Greater exposure to partisan news can cause immediate but short-lived increases in website visits and knowledge of recent events. After adjusting for multiple comparisons, however, we find little evidence of a direct impact on opinions or affect. Still, results from later survey waves suggest that both treatments produce a lasting and meaningful decrease in trust in the mainstream media up to 1 y later. Consistent with the minimal-effects tradition, direct consequences of online partisan media are limited, although our findings raise questions about the possibility of subtle, cumulative dynamics. The combination of experimentation and computational social science techniques illustrates a powerful approach for studying the long-term consequences of exposure to partisan news.
Automated Text Classification of News Articles: A Practical Guide.
Co-authored with Amber E. Boydstun, Suzanna Linn, Ryan McMahon, and Jonathan Nagler
Link | Preprint | Supplementary Materials | Replication code and data | Expand abstract »
Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone of New York Times coverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than pre-defined subject categories provided by news archives. We demonstrate the benefits of coding using article-segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outperform dictionaries on a number of criteria. Overall, we intend this guide to serve as a reminder to analysts that thoughtfulness and human validation are key to text-as-data methods, particularly in an age when it is all-too-easy to computationally classify texts without attending to the methodological choices therein.
Social Media, Echo Chambers, and Political Polarization
Chapter in "Social Media and Democracy: The State of the Field", edited by Nate Persily and Joshua Tucker, Cambridge University Press
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Digital technologies are commonly believed to exarcebate political polarization because they foster the emergence of echo chambers where extremist ideas are amplified. However, empirical studies offer a much more nuanced view of this debate: even if most political exchanges on social media take place among people with similar ideas, cross-cutting interactions are more frequent than commonly believed, exposure to diverse news is higher than through other types of media and ranking algorithms do not have a large impact on the ideological balance of news consumption on Facebook or Google. This chapter offers an exhaustive review of the literature on the link between social media and political polarization, highlighting the areas where a consensus based on empirical evidence has already emerged while identifying the questions that remain open.
Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data.
American Political Science Review
Co-authored with Andreu Casas, Jonathan Nagler, Patrick J. Egan, Richard Bonneau, John T. Jost, and Joshua Tucker
Link | Supplementary Materials | Topic visualization demo | Replication code and data | Expand abstract »
Are legislators responsive to the priorities of the public? Research demonstrates a strong correspondence between the issues about which the public cares and the issues addressed by politicians, but conclusive evidence about who leads whom in setting the political agenda has yet to be uncovered. We answer this question with fine-grained temporal analyses of Twitter messages by legislators and the public during the 113th US Congress. After employing an unsupervised method that classifies tweets sent by legislators and citizens into topics, we use vector autoregression models to explore whose priorities more strongly predict the relationship between citizens and politicians. We find that legislators are more likely to follow, than to lead, discussion of public issues, results that hold even after controlling for the agenda-setting effects of the media. We also find, however, that legislators are more likely to be responsive to their supporters than to the general public.
From Liberation to Turmoil: Social Media and Democracy
Journal of Democracy
, 2017. Co-authored with Joshua Tucker, Yannis Theocharis, and Margaret Roberts.
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How can one technology—social media—simultaneously give rise to hopes for liberation in authoritarian regimes, be used for repression by these same regimes, and be harnessed by antisystem actors in democracy? We present a simple framework for reconciling these contradictory developments based on two propositions: 1) that social media give voice to those previously excluded from political discussion by traditional media, and 2) that although social media democratize access to information, the platforms themselves are neither inherently democratic nor nondemocratic, but represent a tool political actors can use for a variety of goals, including, paradoxically, illiberal goals.
The New Public Address System: Why Do World Leaders Adopt Social Media?
International Studies Quarterly
, 2017. Co-authored with Thomas Zeitzoff.
Link | Preprint | Online Appendix | Replication materials | Expand abstract »
The emergence of social media - and in particular Twitter and Facebook - has led scholars to focus on its effects on mass behavior and protest. Yet an important, and unanswered question is what explains the variation in the adoption and use of social media by world leaders? By the end of 2014, over 76% of world leaders had an active presence on social media, and used their accounts to communicate with domestic and international audiences. We look at several different potential hypotheses that explain adoption of social media by world leaders including: modernization, social pressure, level of democratization, and diffusion. We find strong support that increased political pressure from social unrest and higher levels of democratization are both associated with leader adoption of social media platforms. Although the association we identify is not causal, these findings reveal the relationship between institutional and political pressures and the political communication of country leaders.
The Critical Periphery in the Growth of Social Protests
, 2015, 10 (11).
Co-authored with Ning Wang, Richard Bonneau, John T. Jost, Jonathan Nagler, Joshua Tucker and Sandra González-Bailón
Link | Online appendix | Replication data | Expand abstract »
Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as “slacktivists” because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.
Tweeting from Left to Right: Is Online Political Communication More Than an Echo Chamber?
, 2015, 26 (10), 1531-1542.Co-authored with John T. Jost, Jonathan Nagler, Joshua Tucker, and Richard Bonneau.
Link | Online appendix | Replication materials and data | Expand abstract »
We estimated ideological preferences of 3.8 million Twitter users and, using a dataset of 150 million tweets concerning 12 political and non-political issues, explored whether online communication resembles an "echo chamber" due to selective exposure and ideological segregation or a "national conversation." We observed that information was exchanged primarily among individuals with similar ideological preferences for political issues (e.g., presidential election, government shutdown) but not for many other current events (e.g., Boston marathon bombing, Super Bowl). Discussion of the Newtown shootings in 2012 reflected a dynamic process, beginning as a "national conversation" before being transformed into a polarized exchange. With respect to political and non-political issues, liberals were more likely than conservatives to engage in cross-ideological dissemination, highlighting an important asymmetry with respect to the structure of communication that is consistent with psychological theory and research. We conclude that previous work may have overestimated the degree of ideological segregation in social media usage.
Birds of the Same Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data.
, 2015, 23 (1), 76-91
Link | Pre-print | Online appendix | Replication materials | GitHub tutorial | Expand abstract »
Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this paper I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the US and five European countries. Thee estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.