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.

Are centralized leaders of religious organizations responsive to their followers' political preferences over time even when formal accountability mechanisms, such as elections, are weak or absent? I argue that such leaders have incentives to be responsive because they rely on dedicated members for legitimacy and support. I test this theory by examining the Catholic Church and its centralized leader, the Pope. First, I analyze over 10,000 papal statements to confirm that the papacy is responsive to Catholics' overall political concerns. Second, I conduct survey experiments in Brazil and Mexico to investigate how Catholics react to responsiveness. Catholics increase their organizational trust and participation when they receive papal messages that reflect their concerns, conditional on their existing commitment to the Church and their agreement with the Church on political issues. The evidence suggests that in centralized religious organizations, the leader reaffirms members' political interests because followers support religious organizations that are politically responsive.

While presidents frequently create new policies through unilateral power, empirical scholarship generally focuses on executive orders and overlooks other categories of directives. We introduce data on more than 50,000 unilateral directives issued between 1877 and 2020 and use machine learning techniques to characterize their substantive importance and issue areas. Our measures reveal significant increases in unilateral activity over time, driven largely by increases in foreign affairs and through the substitution of memoranda for executive orders. We use our measures to formally evaluate the historical development of the unilateral presidency and reassess theoretical claims about public opinion and unilateral power. Our research provides new evidence about variation in the use of presidential authority and opens new avenues for empirical inquiry.

This paper examines the strategic use of public news media – specifically television (TV) – as an instrument of political influence, focusing on Italy's 2011 financial crisis under Berlusconi's premiership. Using an original large corpus of over 20,000 hours of televised news transcripts and a quasi-experimental design, we investigate how political influence altered media coverage and, subsequently, public opinion and electoral outcomes. Our difference-in-differences analysis, complemented by unsupervised text scaling of news content, reveals a significant shift from “hard” political news to “soft” news on public TV during Berlusconi's tenure. Findings suggest a deliberate reduction in hard news coverage by an average of 107 seconds daily, which significantly increased voter support for Berlusconi's party. In the conclusions, we discuss the broader implications of our findings for media independence in Western democracies amid the emergence of artificial intelligence-generated news contents and the prevalence of algorithmically tailored news feeds.

Female attorneys at the U.S. Supreme Court are less successful than male attorneys under some conditions because of gender norms, implicit expectations about how men and women should act. While previous work has found that women are more successful when they use more emotional language at oral arguments, gender norms are context sensitive. The COVID-19 pandemic prompted perhaps the most radical contextual shift in Supreme Court history: freewheeling in-person arguments were replaced with turn-based teleconference arguments. This change altered judicial decision-making and, I argue, justices’ assessments of attorneys’ gender performance. Using quantitative textual analysis of oral arguments, I demonstrate that justices implicitly evaluate gender performance with different metrics in each modality. Gender-normative levels of emotional language predict success in both formats. Function words, however, only predict success in teleconference arguments. Given gender’s salience at the Supreme Court and in broader society, my findings prompt questions about the extent to which women can substantively impact case law.

We investigate the gender gap in issue attention among members of parliament (MPs) by applying automated text analytic techniques to a novel data set on Italian parliamentary speeches over a remarkably long period (1948–2020). We detect a gendered specialization across issues that tends to disappear as women’s shares in parliamentary groups increase. We then investigate whether women’s access to previously male-owned issues brings with it a different agenda, operationalized as a different vocabulary. We detect a U-shaped pattern: language gender specificity is high when female MPs are tokens in parliamentary groups with a large preponderance of men; it decreases when their shares start increasing and grows again when they constitute a considerable minority. We argue that this pattern is consistent with the theory of tokenism, and it is produced by the interlinkage of commitment to shared norms and the distribution of “activation thresholds” among female MPs.

Extensive research in Western societies has demonstrated that media reports of protests have succumbed to selection and description biases, but such tendencies have not yet been tested in the Chinese context. This article investigates the Chinese government and news media's selection and description bias in domestic protest events reporting. Using a large protest event data set from Weibo (CASM-China), we found that government accounts on Weibo covered only 0.4 per cent of protests while news media accounts covered 6.3 per cent of them. In selecting events for coverage, the news media accounts tacitly struck a balance between newsworthiness and political sensitivity; this led them to gravitate towards protests by underprivileged social groups and shy away from protests targeting the government. Government accounts on Weibo, on the other hand, eschewed reporting on violent protests and those organized by the urban middle class and veterans. In reporting selected protest events, both government and news media accounts tended to depoliticize protest events and to frame them in a more positive tone. This description bias was more pronounced for the government than the news media accounts. The government coverage of protest events also had a more thematic (as opposed to episodic) orientation than the news media.

It is well known that politicians speak differently when campaigning. The shadow of elections may affect candidates' change in tone during campaigns. However, to date, we lack a systematic study of the changes in communication patterns between campaign and non-campaign periods. In this study, we examine the sentiment expressed in 4.3 million tweets posted by members of national parliaments in the EU27 from 2018 to 2020. Our results show that (1) the opposition, even populists and Eurosceptics, send more positive messages during campaigns, (2) parties trailing in the polls communicate more negatively, and (3) that the changes are similar in national and European elections. These findings show the need to look beyond campaign times to understand parties' appeals and highlight the promises of social media data to move beyond traditional analyses of manifestos and speeches.

Economists have long been interested in the effect of business sentiment on economic activity. Using text analysis, I construct a new company-level indicator of sentiment based on the net balance of positive and negative words in Australian company disclosures. Company-level investment is very sensitive to changes in this corporate sentiment indicator, even controlling for fundamentals, such as Tobin’s Q, as well as controlling for measures of company-level uncertainty.

The high sensitivity of investment to sentiment could be due to several mechanisms. It could be because of animal spirits among managers or because of sentiment proxies for private information held by managers about company prospects. Overall, I find mixed evidence of the underlying causal mechanism. The effect of sentiment on investment is relatively persistent, which is consistent with managers having private information about company fundamentals. But the sensitivity of investment to sentiment is not any stronger at opaque companies in which managers are likely to be better informed than investors. Further, investment is sensitive to sentiment even when investors have an information advantage over managers by lagging the sentiment indicator by a year. Overall, the sensitivity of investment to sentiment appears to reflect both animal spirits and fundamentals.

Corporate investment has been weak since the global financial crisis (GFC) and demand-side factors, such as lower sales growth, explain more than half of this weakness. Low sentiment and heightened uncertainty weighed on investment during the GFC but have been less important factors since then.

Research of judges and courts traditionally centers on judgments, treating each judgment as a unit of observation. However, judgments often address multiple distinct and more or less unrelated issues. Studying judicial behavior on a judgment level therefore loses potentially important details and risks drawing false conclusions from the data. We present a method to assist researchers with splitting judgments by issues using a supervised machine learning classifier. Applying our approach to splitting judgments by the Court of Justice of the European Union into issues, we show that this approach is practically feasible and provides benefits for text-based analysis of judicial behavior.

This research report measures changes in China's public diplomacy after a May 2021 collective study session of the Chinese Communist Party Politburo. The session examined the country's global communications strategy and fuelled speculation about what might change in China's external communications, particularly with regard to its “wolf warrior” diplomats. Combining hand-coding and quantitative text analysis, we develop and validate a measure of “wolf warrior diplomacy” rhetoric and apply it to over 200,000 tweets from nearly 200 institutional, media and diplomatic Twitter accounts. Using a difference-in-difference research design, we evaluate if the session led to a noticeable change in the tweets of diplomats based in OECD countries. After the announcement, PRC diplomats in the OECD moderated their tweets in comparison to non-OECD diplomats, but we do not detect a major re-orientation of PRC communication strategies. These findings have relevance for scholars of Chinese foreign policy, nationalism and public diplomacy.

How do European far-right parties reconcile their long-standing nationalism with their allegiance to European “civilization”? Although they are certainly not contradictory, simultaneously adopting national and supranational identities requires considerable discursive maneuvering to articulate clearly. In this article, I argue that the European Far Right negotiates the boundaries between its national and supranational identities through two discursive mechanisms, abstraction and embedding, which present civilizationism as nonthreatening to and partially constituted by nationalism. Specifically, abstraction links European civilization to general features of a shared heritage, whereas embedding connects civilization to elements of the nationalist repertoire. I demonstrate the Far Right’s monopolization of civilizational discourse and use of these twin mechanisms through quantitative and qualitative analyses of more than 1,000 party manifestos and more than 650,000 tweets. These findings contribute to the growing scholarly literature that treats civilizations as supranational “imagined communities” and has implications for the study of nationalism, civilizationism, and the Far Right.

There is an ongoing debate on whether wine reviews provide meaningful information on wine properties and quality. However, few studies have been conducted aiming directly at comparing the utility of wine reviews and numeric measurements in wine data analysis. Based on data from close to 300,000 wines reviewed by Wine Spectator, we use logistic regression models to investigate whether wine reviews are useful in predicting a wine's quality classification. We group our sample into one of two binary quality brackets, wines with a critical rating of 90 or above and the other group with ratings of 89 or below. This binary outcome constitutes our dependent variable. The explanatory variables include different combinations of numerical covariates such as the price and age of wines and numerical representations of text reviews. By comparing the explanatory accuracy of the models, our results suggest that wine review descriptors are more accurate in predicting binary wine quality classifications than are various numerical covariates—including the wine's price. In the study, we include three different feature extraction methods in text analysis: latent Dirichlet allocation, term frequency-inverse document frequency, and Doc2Vec text embedding. We find that Doc2Vec is the best performing feature extraction method that produces the highest classification accuracy due to its capability of using contextual information from text documents. (JEL Classifications: C45, C88, D83)

How can authoritarian regimes effectively control information to maintain regime legitimacy in times of crisis? We argue that media framing constitutes a subtle and sophisticated information control strategy in authoritarian regimes and plays a critical role in steering public opinion and cultivating an image of competent government during a tremendous crisis. Using structural topic models (STM), we conduct a textual analysis of more than 4,600 news reports produced by seven Chinese media outlets during the COVID-19 pandemic. We find that Chinese media, instructed by the propaganda authorities, used a heroism frame to feature frontline medics’ sacrifices when saving others in need and resorted to a contrast frame to highlight the poor performance of the United States in the fight against COVID-19. We also show that both state and commercial media outlets used these two frames, though the tone of commercial media coverage was generally more moderate than the state media version.

Firms rely on brand names to market goods to consumers, and consumers rely on brand names to locate goods that satisfy their preferences. If multiple firms are using the same or similar names, consumers may be confused about which product to buy, and firms may not obtain the benefits of their investments in quality. Recently, both firms and scholars in a number of industries have expressed concern about brand name congestion—too many firms clustering around too few terms. This paper applies computational linguistic analysis to chateau names in the Bordeaux wine region to study the degree of brand congestion within a mature, traditional, and high-value market. We find that Bordeaux producers have highly similar names to one another, far more than in comparable wine regions such as California and Alsace. More than a quarter of all Bordeaux producers have a name that is identical or nearly so to at least one other producer, and many terms are claimed by dozens of different producers. Interestingly, however, we find that the most famous and renowned producers have names that tend to be more distinctive than their less famous brethren. (JEL Classifications: C88, D83, L66, O34)

We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the method can be more specifically targeted to a research question and the resulting topics are easier to validate and interpret. We demonstrate the method using the case of labeled party platforms (source corpus) and unlabeled parliamentary speeches (target corpus). In addition to the standard within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target-corpus documents. We find that the classifier accurately assigns topics in the parliamentary speeches, although accuracy varies substantially by topic. We also propose tools diagnosing cross-domain classification. To illustrate the usefulness of the method, we present two case studies on how electoral rules and the gender of parliamentarians influence the choice of speech topics.