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Which countries are likely to be subjects of research in the study of armed conflict? Evidence from other fields suggests that research often focuses disproportionately on the West, but it is unclear to what extent this is true in conflict studies. We suggest that a baseline explanation of research focus is each country's conflict experience, and we present two additional hypotheses: Western bias and research feasibility. Empirically, we count countries in the abstracts of five prominent conflict or security journals, 1990–2015. We also manually count single-country case studies. Western countries, measured by U.N. voting or geographic location, appear more than non-Western countries, even after considering conflict, wealth, and other factors. There is less support for the research feasibility argument, measured by each country's official languages and democracy. We find French- and Spanish-language countries less likely to appear in the literature. We conclude with a discussion of under-studied countries and offer related suggestions.

This manuscript helps to resolve the ongoing debate concerning the effect of information communication technology on human rights monitoring. We reconceptualize human rights as a taxonomy of nested rights that are judged in textual reports and argue that the increasing density of available information should manifest in deeper taxonomies of human rights. With a new automated system, using supervised learning algorithms, we are able to extract the implicit taxonomies of rights that were judged in texts by the US State Department, Amnesty International, and Human Rights Watch over time. Our analysis provides new, clear evidence of change in the structure of these taxonomies as well as in the attention to specific rights and the sharpness of distinctions between rights. Our findings bridge the natural language processing and human rights communities and allow a deeper understanding of how changes in technology have affected the recording of human rights over time.

Sentiment, judgments and expressed positions are crucial concepts across international relations and the social sciences more generally. Yet, contemporary quantitative research has conventionally avoided the most direct and nuanced source of this information: political and social texts. In contrast, qualitative research has long relied on the patterns in texts to understand detailed trends in public opinion, social issues, the terms of international alliances, and the positions of politicians. Yet, qualitative human reading does not scale to the accelerating mass of digital information available currently. Researchers are in need of automated tools that can extract meaningful opinions and judgments from texts. Thus, there is an emerging opportunity to marry the model-based, inferential focus of quantitative methodology, as exemplified by ideal point models, with high resolution, qualitative interpretations of language and positions. We suggest that using alternatives to simple bag of words (BOW) representations and re-focusing on aspect-sentiment representations of text will aid researchers in systematically extracting people's judgments and what is being judged at scale. The experimental results below show that our approach which automates the extraction of aspect and sentiment MWE pairs, outperforms BOW in classification tasks, while providing more interpretable parameters. By connecting expressed sentiment and the aspects being judged, PULSAR (Parsing Unstructured Language into Sentiment-Aspect Representations) also has deep implications for understanding the underlying dimensionality of issue positions and ideal points estimated with text. Our approach to parsing text into aspects-sentiment expressions recovers both expressive phrases (akin to categorical votes), as well as the aspects that are being judged (akin to bills). Thus, PULSAR or future systems like it, open up new avenues for the systematic analysis of high-dimensional opinions and judgments at scale within existing ideal point models.

There is an ongoing debate about whether human rights standards have changed over the last 30 years. The evidence for or against this shift relies upon indicators created by human coders reading the texts of human rights reports. To help resolve this debate, we suggest translating the question of changing standards into a supervised learning problem. From this perspective, the application of consistent standards over time implies a time-constant mapping from the textual features in reports to the human coded scores. Alternatively, if the meaning of abuses have evolved over time, then the same textual features will be labeled with different numerical scores at distinct times. Of course, while the mapping from natural language to numerical human rights score is a highly complicated function, we show that these two distinct data generation processes imply divergent overall patterns of accuracy when we train a wide variety of algorithms on older versus newer sets of observations to learn how to automatically label texts with scores. Our results are consistent with the expectation that standards of human rights have changed over time.