Content analysis, a labor-intensive but widely-applied research method, is increasingly being supplemented by computational techniques such as statistical topic modeling. However, while the discourse on content analysis centers heavily on reproducibility, computer scientists often focus more on increasing the scale of analysis and less on establishing the reliability of analysis results. The gap between user needs and available tools leads to justified skepticism, and limits the adoption and effective use of computational approaches. We argue that enabling human-in-the-loop machine learning requires establishing users’ trust in computer-assisted analysis. To this aim, we introduce our ongoing work on analysis tools for interac- tively exploring the space of available topic models. To aid tool development, we propose two studies to examine how a computer-aided workflow affects the uncovered codes, and how machine-generated codes impact analysis outcome. We present our prototypes and findings currently under submission.
This vignette introduces the lbfgs package for R, which consists of a wrapper built around the libLBFGS optimization library written by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1 norm of the problem’s parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems. The lbfgs package compares favorably with other optimization packages for R in microbenchmark tests.
In this paper I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiple types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although related models have previously been proposed in several different fields, inference is typically cumbersome and slow. I introduce a class of fast variational inference algorithms that allow for models to be fit quickly and accurately.
Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semi-automated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author’s gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.
Awarded the Gosnell Prize for Excellence in Political Methodology for the best work in political methodology presented at any political science conference during the preceding year. Data at: http://dx.doi.org/10.7910/DVN/29405
In examining the diffusion of social and political phenomena like regime transition, conflict, and policy change, scholars routinely make choices about how proximity is defined and which neighbors should be considered more important than others. Since each specification offers an alternative view of the networks through which diffusion can take place, one’s decision can exert a significant influence on the magnitude and scope of estimated diffusion effects. This problem is widely recognized, but is rarely the subject of direct analysis. In international relations research, connectivity choices are usually ad hoc, driven more by data availability than by theoretically informed decision criteria. We take a closer look at the assumptions behind these choices, and propose a more systematic method to asses the structural similarity of two or more alternative networks, and select one that most plausibly relates theory to empirics. We apply this method to the spread of democratic regime change, and offer an illustrative example of how neighbor choices might impact predictions and inferences in the case of the 2011 Arab Spring.
Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have prevented political scientists from using texts in their research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods. Automated text methods are useful, but incorrect, models of language: they are no substitute for careful thought and close reading. Rather, automated text methods augment and amplify human reading abilities. Using the methods requires extensive validation in any one application. With these guiding principles to using automated methods, we clarify misconceptions and errors in the literature and identify open questions in the application of automated text analysis in political science. For scholars to avoid the pitfalls of automated methods, methodologists need to develop new methods specifically for how social scientists use quantitative text methods.
Russia’s intervention in the Georgian–South Ossetian conflict has highlighted the need to rigorously examine trends in the public debate over the use of force in Russia. Approaching this debate through the prism of civil–military relations, we take advantage of recent methodological advances in automated content analysis and generate a new dataset of 8000 public statements made by Russia’s political and military leaders during the Putin period. The data show little evidence that military elites exert a restraining influence on Russian foreign and defence policy. Although more hesitant than their political counterparts to embrace an interventionist foreign policy agenda, Russian military elites are considerably more activist in considering the use of force as an instrument of foreign policy.
This study addresses the factors that lead individuals to flee their homes in search of refuge. Many argue that individuals abandon their homes in favor of an uncertain life elsewhere because of economic hardship, while others argue that threats to their lives, physical person, and liberty cause them to flee. This study engages the debate by analyzing flight patterns over time from Haiti to the United States as a function of economic and security factors. Which factors have the largest influence on Haitian-U.S. migratory patterns? Our results show that both economics and security play a role. However, our analyses are able to distinguish between the effects of different individual economic and security indicators on Haitian-U.S. migration.
This study predicts forced migration events by predicting the civil violence, poor economic conditions, and foreign interventions known to cause individuals to flee their homes in search of refuge. If we can predict forced migration, policy-makers can better plan for humanitarian crises. While the study is limited to predicting Haitian flight to the United States, its strength is its ability to predict weekly flows as opposed to annual flows, providing a greater level of predictive detail than its ‘country-year’ counterparts. We focus on Haiti given that it exhibits most, if not all, of the independent variables included in theories and models of forced migration. Within our temporal domain (1994–2004), Haiti experienced economic instability, low intensity civil conflict, state repression, rebel dissent, and foreign intervention and influence. Given the model’s performance, the study calls for the collection of disaggregated data in additional countries to provide more precise and useful early-warning models of forced migrant events.
This paper examines the effects of source bias on statistical inferences drawn from event data analyses. Most event data projects use a single source to code events. For example most of the early Kansas Event Data System (KEDS) datasets code only Reuters and Agence France Presse (AFP) reports. One of the goals of Project Civil Strife (PCS) –a new internal conflict-cooperation event data project– is to code event data from several news sources to garner the most extensive coverage of events and control for bias often found in a single source. Herein, we examine the effects that source bias has on the inferences we draw from statistical time-series models. In this study, we examine domestic political conflict in Indonesia and Cambodia from 1980-2004 using automated content analyzed datasets collected from multiple sources (i.e. Associated Press, British Broadcasting Corporation, Japan Economic Newswire, United Press International, and Xinhua). The analyses show that we draw different inferences across sources, especially when we disaggregate domestic political groups. We then combine our sources together and eliminate duplicate events to create a multi-source dataset and compare the results to the single-source models. We conclude that there are important differences in the inferences drawn dependent upon source use. Therefore, researchers should (1) check their results across multiple sources and/or (2) analyze multi-source data to test hypotheses when possible.