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We demonstrate that social media data represent a useful resource for testing models of legislative and individual-level political behavior and attitudes. First, we develop a model to estimate the ideology of politicians and their supporters using social media data on individual citizens' endorsements of political figures. Our measure allows us to place politicians and more than 6 million citizens who are active in social media on the same metric. We validate the ideological estimates that result from the scaling process by showing they correlate highly with existing measures of ideology from Congress, and with individual-level self-reported political views. Finally, we use these measures to study the relationship between ideology and age, social relationships and ideology, and the relationship between friend ideology and turnout.

Much of the literature on polarization and selective exposure presumes that the internet exacerbates the fragmentation of the media and the citizenry. Yet this ignores how the widespread use of social media changes news consumption. Social media provide readers a choice of stories from different sources that come recommended from politically heterogeneous individuals, in a context that emphasizes social value over partisan affiliation. Building on existing models of news selectivity to emphasize information utility, we hypothesize that social media's distinctive feature, social endorsements, trigger several decision heuristics that suggest utility. In two experiments, we demonstrate that stronger social endorsements increase the probability that people select content and that their presence reduces partisan selective exposure to levels indistinguishable from chance.

Previous research demonstrates that opposition to immigration increases when immigrant groups are seen as economically non-viable and burdened with distinctive and unassimilable cultural practices. However, no research to date has investigated the parallel relationship between attributes of individuals and willingness to admit these individuals as legal immigrants. This study examines Norwegians' evaluations of individual immigrants. Using an experimental design, specific attributes of immigrants are manipulated, making them appear more or less likely to make an economic contribution and more or less likely to assimilate into Norwegian culture. It is found that the decision to admit individuals is predominantly influenced by the immigrant's economic background. Norwegians are especially supportive of highly skilled immigrants. The immigrant's race is also relevant, but the effect of racial cues varies between men and women. Immigrants with an Afrocentric appearance are more likely to be rejected by men, but accepted by women. The article recommends that immigration researchers measure public support for immigration at both the policy and individual immigrant levels. Adapted from the source document.

Previous research demonstrates that opposition to immigration increases when immigrant groups are seen as economically non‐viable and burdened with distinctive and unassimilable cultural practices. However, no research to date has investigated the parallel relationship between attributes of individuals and willingness to admit these individuals as legal immigrants. This study examines Norwegians' evaluations of individual immigrants. Using an experimental design, specific attributes of immigrants are manipulated, making them appear more or less likely to make an economic contribution and more or less likely to assimilate into Norwegian culture. It is found that the decision to admit individuals is predominantly influenced by the immigrant's economic background. Norwegians are especially supportive of highly skilled immigrants. The immigrant's race is also relevant, but the effect of racial cues varies between men and women. Immigrants with an Afrocentric appearance are more likely to be rejected by men, but accepted by women. The article recommends that immigration researchers measure public support for immigration at both the policy and individual immigrant levels.

Constituents often fail to hold their representatives accountable for federal spending decisions-even though those very choices have a pervasive influence on American life. Why does this happen? Breaking new ground in the study of representation, The Impression of Influence demonstrates how legislators skillfully inform constituents with strategic communication and how this facilitates or undermines accountability. Using a massive collection of Congressional texts and innovative experiments and methods, the book shows how legislators create an impression of influence through credit claiming messages.Anticipating constituents' reactions, legislators claim credit for programs that elicit a positive response, making constituents believe their legislator is effectively representing their district. This spurs legislators to create and defend projects popular with their constituents. Yet legislators claim credit for much more-they announce projects long before they begin, deceptively imply they deserve credit for expenditures they had little role in securing, and boast about minuscule projects. Unfortunately, legislators get away with seeking credit broadly because constituents evaluate the actions that are reported, rather than the size of the expenditures.The Impression of Influence raises critical questions about how citizens hold their political representatives accountable and when deception is allowable in a democracy

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

Particularistic spending, a large literature argues, builds support for incumbents. This literature equates money spent in the district with the credit constituents allocate. Yet, constituents lack the necessary information and motivation to allocate credit in this way. We use extensive observational and experimental evidence to show how legislators' credit claiming messages—and not just money spent in the district—affect how constituents allocate credit. Legislators use credit claiming messages to influence the expenditures they receive credit for and to affect how closely they are associated with spending in the district. Constituents are responsive to credit claiming messages—they build more support than other nonpartisan messages. But contrary to expectations from other studies, constituents are more responsive to the total number of messages sent rather than the amount claimed. Our results have broad implications for political representation, the personal vote, and the study of U.S. Congressional elections.