While members of Congress now routinely communicate with constituents using images on a variety of internet platforms, little is known about how images are used as a means of strategic political communication. This is due primarily to computational limitations which have prevented large-scale, systematic analyses of image features. New developments in computer vision, however, are bringing the systematic study of images within reach. Here, we develop a framework for understanding visual political communication by extending Fenno's analysis of home style (Fenno 1978) to images and introduce "photographic" home styles. Using approximately 192,000 photographs collected from MCs Facebook profiles, we build machine learning software with convolutional neural networks and conduct an image manipulation experiment to explore how the race of people that MCs pose with shape photographic home styles. We find evidence that electoral pressures shape photographic home styles and demonstrate that Democratic and Republican members of Congress use images in very different ways.
Racially segregated cities tend to be politically polarized cities, leading to inequalities in public goods provision, political and social isolation, concentrated poverty and the perpetuation of a sense of hopelessness among many living in America’s urban centers. While the links between racial segregation and political polarization are well established, it is less clear why, or through what mechanism, both arise simultaneously. In this article, we derive a formal model which we demonstrate can partially account for this puzzle. This model allows us to derive “ideological tipping points”: changes in neighborhood demographics at which all members of one or more groups along the ideological spectrum (liberal, conservative, moderate) relocate. We then validate the model and demonstrate that racial segregation and political polarization consistently emerge in equilibrium under a wide variety of conditions by simulating movement of individuals between Census tracts in the largest 10 cities in the United States.
There is growing concern among journalists and scholars about the remarkable influence of online commentators. Studies exploring the impacts of negative comments on scientific news stories find that they tend to undermine public knowledge about science and cause increased skepticism of well-established scientific facts (Anderson et al., 2014; Kanuka and Anderson, 2007; Coe, Kenski and Rains, 2014). In response to a growing sense that anonymous “trolling” has gotten out of hand, online magazines and newspapers such as Salon.com have begun banning anonymous online posts (Diakopoulos, 2015). As more and more political discourse moves online and anonymous online discussion becomes the norm, understanding how anonymous commentary affects political views and interpretations of online content will become even more important. Indeed, we are seeing a massive shift in how Americans obtain information and acquire information—but how are these shifts affecting political opinions? This study aims to (1) conceptualize the political roles and functions of online comments and (2) use an online survey experiment to explore how the comments themselves can affect readers’ political attitudes.
Innovative natural experiments, observational research and theories of racial threat suggest that skin tone is a determinant of nativist sentiment, yet experiments which include immigrant skin tone as a treatment find little connection between the two. We argue that these contradictory findings can be partially explained by experimental designs which exclude information about immigrant geographic context, an essential component of threat. To address these issues, we design a survey experiment in which geographic context and immigrant skin tone are randomly manipulated. We find that skin tone has potent effects on support for anti-immigration policy when geographic context is included but has no effects when context is excluded. We argue that these results suggest that geographic context should be considered in future experiments which seek to measure the effects of immigrant skin tone on policy outcomes.
In this paper I simulate neighborhood level political migration dynamics following a change in neighborhood racial composition using SimPolSeg, an original agent-based modeling software program. SimPolSeg simulates agent behavior according to the Migration-Polarization (MP) theory of partisan sort- ing (Anastasopoulos 2015a). Dynamic simulations using SimPolSeg demon- strate how non-white migration and conservative ight lead to racially and ideologically segregated urban neighborhoods.
While the number of female candidates running for office in U.S. House of Representative elections has increased considerably since the 1980s, women continue to account for about only 20\% of House members. Whether this gap in female representation can be explained by a gender penalty female candidates face as the result of discrimination on the part of voters or campaign donors remains uncertain. In this paper, I estimate the gender penalty in U.S. House of Representative general elections using a regression discontinuity design (RDD). Using this RDD, I am able to assess whether chance nomination of female candidates to run in the general election affected the amount of campaign funds raised, general election vote share and probability of victory in House elections between 1982-2012. I find no evidence of a gender penalty using these measures. These results suggest that the deficit of female representation in the House is more likely the result of barriers to entering politics as opposed to overt gender discrimination by voters and campaign donors.