Cross-racial mobilization played an important role in explaining the Latino turnout for Barack Obama in the 2012 election

In 2012, Latino turnout increased by more than a quarter, despite a fall in overall turnout that election year. The vast majority of this increased vote went to Obama. Why was Obama so successful with Latino voters? In new research, Loren Collingwood finds that a model that goes beyond voter demographics, and takes into account the Obama campaign’s outreach to Latinos and policy stances such as deferred action for undocumented immigrants helps to explain this success. He writes that when taking into account Obama’s ability to tap into the shared racial and ethnic identities of Latinos, voters were up to 70 percent more likely to vote for him because of this engagement

In the wake of the Parkland mass shooting, the public's now continual anxiety about gun crime may lead to a greater push for stricter gun laws

The shooting at Marjory Stoneman Douglas High School last month has put the issue of gun control back on to the public and political agendas in the US. In light of this latest mass shooting, Alexandra Filindra and Loren Collingwood look at the relationship between the public's response to such events and their attitudes towards gun control. They find that after such shootings, there is an increase in public anxiety about crime, which is then linked to greater support for more restrictive gun laws. While this anxiety often goes away in time, they write that as mass shootings become more common – and more deadly – a sustained sense of public anxiety may lead to more long-lasting public support for gun control

During the election, Donald Trump's racist rhetoric activated the fears of people in areas with growing Latino populations

In the opening salvo of his presidential election campaign in June 2015, Donald Trump referred to Mexican immigrants as "racists" and "criminals". In new research Ben Newman, Sono Shah, and Loren Collingwood look at how Trump's racist rhetoric drew support during the election. Using data from survey polls taken during the campaign, they find that Trump's inflammatory comments activated the latent anti-immigrant sentiments of those who lived in areas which had experienced large increases in their Latino populations

Protests against Trump’s immigration executive order may have helped shift public opinion against it.

Donald Trump’s executive order preventing the entry of refugees and those from seven Muslim-majority countries has sparked protests across the country and the world. But have those protests had an effect on public opinion? Loren Collingwood, Nazita Lajevardi, and Kassra Oskooii present preliminary findings from a survey conducted before and after President Trump’s executive order. They find that after the ban, 25 percent more Democrats and an additional 15 percent of Republicans became opposed to it. In addition, one in five of all respondents stated that the protests had an impact on their views towards immigration policy

RTextTools: A Supervised Learning Package for Text Classification

Social scientists have long hand-labeled texts to create datasets useful for studying topics from congressional policymaking to media reporting. Many social scientists have begun to incorporate machine learning into their toolkits. RTextTools was designed to make machine learning accessible by providing a start-to-finish product in less than 10 steps. After installing RTextTools, the initial step is to generate a document term matrix. Second, a container object is created, which holds all the objects needed for further analysis. Third, users can use up to nine algorithms to train their data. Fourth, the data are classified. Fifth, the classification is summarized. Sixth, functions are available for performance evaluation. Seventh, ensemble agreement is conducted. Eighth, users can cross-validate their data. Finally, users write their data to a spreadsheet, allowing for further manual coding if required

Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods

Text is becoming a central source of data for social science research. With advances in digitization and open records practices, the central challenge has in large part shifted away from availability to usability. Automated text classification methodologies are becoming increasingly important within political science because they hold the promise of substantially reducing the costs of converting text to data for a variety of tasks. In this paper, we consider a number of questions of interest to prospective users of supervised learning methods, which are appropriate to classification tasks where known categories are applied. For the right task, supervised learning methods can dramatically lower the costs associated with labeling large volumes of textual data while maintaining high reliability and accuracy. Information science researchers devote considerable attention to comparing the performance of supervised learning algorithms and different feature representations, but the questions posed are often less directly relevant to the practical concerns of social science researchers. The first question prospective social science users are likely to ask is — how well do such methods work? The second is likely to be — how much do they cost in terms of human labeling effort? Relatedly, how much do marginal improvements in performance cost? We address these questions in the context of a particular dataset — the Congressional Bills Project — which includes more than 400,000 labeled bill titles (19 policy topics). This corpus also provides opportunities to experiment with varying sample sizes and sampling methodologies. We are ultimately able to locate an accuracy/efficiency sweet spot of sorts for this dataset by leveraging results generated by an ensemble of supervised learning algorithms

The Pursuit of Victory and Incorporation: Elite Strategy, Group Pressure, and Cross-Racial Mobilization

Thesis (Ph.D.)--University of Washington, 2012Cross-racial mobilization (CRM) is conscious race-targeted mobilization of blocs of voters of one racial group by politicians and campaign operatives of another racial group. To date, no theoretical framework has articulated the processes and myriad components of CRM. This dissertation fills that void. The four research questions posed are: Does cross-racial mobilization happen on a measurable basis? Under what conditions is cross-racial mobilization most likely to happen? Does it tend to work? And does cross-racial mobilization produce a by-product of enhanced minority participation? As the U.S. diversifies, and as the ramifications of the 2012 presidential election materialize, the entry of new racial groups into the electorate brings new strategic considerations and constraints to bear on political elites -- in the process changing when and how campaigns mobilize minority voters. Using a series of historical and contemporary case studies and mixed-methods, I demonstrate that CRM has been a constant feature of some states' post-war politics, and that the key elements driving CRM outcomes are in-group characteristics such as the hostility of the white constituency (white backlash), and out-group characteristics such as the organizational capacity, size, and growth-rate of the minority constituency. In addition, institutional barriers -- such as the poll tax and restrictive immigration laws-- negatively affect CRM opportunities. Competitiveness of the election and whether an opposing candidate is engaged in CRM also influence whether a candidate mobilizes members of another racial group. With respect to the third question, I find that increases in CRM tend to associate with increases in vote share. Finally, I find that in their pursuit of votes, candidates often contribute to increases in voter registration and turnout among minority populations -- thereby forging a link between political elites and historically under-represented populations

Group-based appeals and the Latino vote in 2012: How immigration became a mobilizing issue

a b s t r a c t We evaluate a theory of campaign learning in the context of immigration and the 2012 Latino vote. Following events in Nevada and Arizona after the 2008 election and prior to the 2012 election, we argue and show that Obama's campaign team learned from several Democratic U.S. Senate campaigns in how best to mobilize the Latino vote on the issue of immigration. As a result, we argue, this campaign learning led to an increase in the Latino vote for Obama. To demonstrate this, we compare a group-based appeals model against a traditional vote-choice model, and show that variables measuring Latino Outreach had the greatest impact on the 2012 Latino vote e above and beyond party identification and other traditional vote-choice predictors