An extensive theoretical and practitioner literature addresses the drivers and consequences of transformation of violent rebel actors during conflicts. However, measurement challenges constrain large-N empirical study of the effects and consequences of such transformations. This Research Note introduces a strategy to identify periods of transformation and change in the operation of non-state armed militant groups via computational text analysis of trends in reporting on activities. It presents the measurement approach and demonstrates scalability to a corpus of more than 200 militant groups operating from 1989 to 2020. The study concludes by extending a recent analysis of the impacts of uncertainty on conflict termination. An online Appendix demonstrates the advantages and drawbacks of the measurement through a series of case studies.

This paper presents a topic modeling approach to corpus-based dialectometry. Topic models are most often used in text mining to find latent structure in a collection of documents. They are based on the idea that frequently co-occurring words present the same underlying topic. In this study, topic models are used on interview transcriptions containing dialectal speech directly, without any annotations or preselected features. The transcriptions are modeled on complete words, on character n-grams, and after automatical segmentation. Data from three languages, Finnish, Norwegian, and Swiss German, are scrutinized. The proposed method is capable of discovering clear dialectal differences in all three datasets, while reflecting the differences between them. The method provides a significant simplification of the dialectometric workflow, simultaneously saving time and increasing objectivity. Using the method on non-normalized data could also benefit text mining, which is the traditional field of topic modeling.

Social network analysis is known to provide a wealth of insights relevant to many aspects of policymaking. Yet, the social data needed to construct social networks are not always available. Furthermore, even when they are, interpreting such networks often relies on extraneous knowledge. Here, we propose an approach to infer social networks directly from the texts produced by actors and the terminological similarities that these texts exhibit. This approach relies on fitting a topic model to the texts produced by these actors and measuring topic profile correlations between actors. This reveals what can be called “hidden communities of interest,” that is, groups of actors sharing similar semantic contents but whose social relationships with one another may be unknown or underlying. Network interpretation follows from the topic model. Diachronic perspectives can also be built by modeling the networks over different time periods and mapping genealogical relationships between communities. As a case study, the approach is deployed over a working corpus of academic articles (domain of philosophy of science; N=16,917).

This study reviews 71 high-quality studies of massive open online courses focused on languages (LMOOCs) that were published from the inception of LMOOCs to 2021. The purpose of this study is to gain a deeper understanding of the current state of research and identify fruitful directions for future LMOOC research. First, we reviewed three basic sets of characteristics of these studies: (1) research trends – for example, publication types and years; (2) research contexts – for example, countries in which the studies were conducted, the subjects’ target languages, language-ability levels, skills, and whether the focal courses are for specific purposes; and (3) research design, including data collection, data analysis, and theoretical frameworks. We then utilized a text-mining approach called Latent Dirichlet Allocation that uses machine-learning techniques to identify research-topic commonalities underlying the collected studies. In this way, a total of nine topics were identified. They were: (1) core elements of LMOOCs; (2) interaction and communication in LMOOCs; (3) innovative LMOOC teaching practices; (4) LMOOC standards and quality assurance; (5) LMOOC implementation, participation, and completion; (6) LMOOC teaching plans; (7) LMOOC learning effectiveness and its drivers/obstacles; (8) learners and learning in LMOOCs; and (9) inclusiveness in LMOOCs. These were then diagrammed as a ThemeRiver, which showed the evolutionary trend of the nine identified topics. Specifically, scholarly interest in Topics 5, 7, and 9 increased over time, whereas for Topics 1 and 6, it decreased. Based on our results, we highlighted specific directions for future LMOOC research on each of the identified research topics.

Quantitative approaches are gaining popularity in German legal research. The analysis of large corpora of legal text may be supported by text mining methods. In this study, we employ topic modeling—which aims at retrieving the “topics” of a corpus—to identify words related to certain areas of law present in the case law of the German Federal Constitutional Court (FCC). This information is then evaluated by legal experts and used to show significant content-related differences between the two most frequent types of proceedings before the FCC. Technical and somewhat unstable areas of law, such as tax law, social law, and civil service law, are significantly overrepresented in referrals for judicial review, whereas areas of law characterized by well-developed case law and judicial doctrine appear substantially more often in constitutional complaints. This insight may come as a surprise due to the fact that the Court’s material scope of review is identical in both types of proceedings. Our considerations do not, however, seem to apply to private law. Though we recognize the methodological and epistemological concerns regarding the heuristic nature of topic modeling, this study exemplifies its productive use in complementing, rather than replacing, more traditional techniques of analysis in legal studies.

This article analyzes questions of literary production and literary value in the Democratic People's Republic of Korea (DPRK) using quantitative methods and data sets covering a significant part of the country's literary and intellectual output over forty years (1977–2016). The first part of the article adopts a thematic approach, using topic modeling and time series, to map the DPRK's literary output and investigate the influence of genre on literary consecration. The second part looks at questions of style and literary quality in the DPRK through a comparative analysis of multiple novels grouped by degree of literary prestige. It introduces a method to evaluate the originality of an author's writing style using a masked language model (BERT) and uses it to assess the role of conformity and originality in the DPRK's literary field. Even though writers consecrated by the state display a high level of stylistic conformity when writing mandated biographies of the ruling family, their independent output ranks higher in originality than nonconsecrated writers.

Large-scale coordinated efforts have been dedicated to understanding the global health and economic implications of the COVID-19 pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations has largely been neglected. Understanding public attitudes toward migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources to monitor public opinion are often limited, notably due to slow collection and release activities. New forms of data, particularly from social media, can help overcome these limitations. While some bias exists, social media data are produced at an unprecedented temporal frequency, geographical granularity, are collected globally and accessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this article aims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemic in Germany, Italy, Spain, the United Kingdom, and the United States. Results show an increase of migration-related Tweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence of a significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset by comparable increases in positive messages. Additionally, we presented evidence of growing social polarization concerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

I use a computational linguistic algorithm to measure the topics covered in textual descriptions of wine. I ask whether there is information in the text that consumers value. Wine is a prominent example of an experience good. There is substantial product differentiation in the market, and consumers only have limited information on the utility they will receive when consumed. Thus, information is expected to be valuable. Evaluating descriptions of wine produced across the United States, I use a hedonic price regression to explore whether the descriptions provide any new information not already available to the consumer. Initial results suggest that text descriptions are shown to lose their explanatory value when varietal and numerical ratings are included as controls. I then show that once the varietal, region, and numerical ratings are adequately controlled for, there is information in the descriptions that consumers value. (JEL Classifications: C81, D83, L15)