How has Russia been studied by political scientists, economists, and scholars in cognate fields who publish in specialized area-specific journals studied Russia? To systematically analyze the approaches employed in Russian studies over the last decade, I collected all publications (1,097 articles) on the country from the top five area studies journals covering the territories of the former USSR, the top 10 journals in political science, and the top five journals in economics from January 2010 to January 2022 and classified them based on the methods they utilized, empirical focus, and sub-fields within method. In this article, I discuss the results of this classification and the pitfalls associated with over-reliance on some methods over others, notably those that include self-reported data, in the context of Russia’s war against Ukraine and the increasingly repressive domestic environment under Putin’s autocracy. I also propose some ways of addressing the new realities of diminished access to data and fieldwork.

Keywords: Russia, Russian Studies, methods, surveys, survey bias, economics, political science

Post-conflict states increasingly hold domestic war crimes trials. But do they prosecute perpetrators fairly? Applying statistical modeling and quantitative text analysis to two original datasets based on 555 decisions delivered to Serb and non-Serb defendants in Serbia’s war crimes trials (1999–2019), we do not find evidence of ethnic bias but demonstrate conflict actor bias. Paramilitaries received harsher sentences than state agents of violence, such as army members, from the same ethnic group for the same offenses. Additionally, we show that bias manifests in the verdicts’ textual content. Paramilitary violence is depicted more extensively and with greater detail than crimes committed by state actors. We demonstrate how deniability of accountability, which incentivizes government collusion with paramilitaries during conflict, operates after conflict. A state cannot completely avoid criminal responsibility given the global norm of accountability. Nonetheless, it can use domestic prosecutions to minimize state wrongdoing by associating egregious violence with paramilitaries.

Keywords: domestic war crimes trials, ethnic bias, paramilitaries, Serbia, deniability, human rights violations

Many present-day dictators enjoy genuine public support and use state-controlled media to manufacture it. However, news-media management strategies employed in autocracies are understudied by political scientists. In the case of Russia, some scholars argue that on domestic media, Vladimir Putin primarily aims to be seen as an advocate of national interests abroad, while others suggest that state-controlled media are mostly focused on portraying him as a competent manager at home. Drawing on 398,703 reports from television network Channel One and applying a machine learning algorithm to classify the reports, I show that from December 1998 to June 2022, Vladimir Putin has been primarily covered in domestic stories. However, I also demonstrate that the share of news about foreign affairs and events abroad that mentions the ruler has been increasing every year since 2013. These findings contribute to the literature on Vladimir Putin’s popularity and to broader research on autocratic resilience.

Keywords: Putin, Russia, television, autocracy, autocratic resilience

Do autocracies employ state-controlled media in an attempt to prime domestic audiences for external wars of aggression?  This article is the first study to expose large-scale evidence of attempts by Russian state-controlled domestic television to amplify for its audience the importance of the Ukrainian agenda in the years prior to the full-scale war. Using 2.4 million transcripts of news stories transmitted in Russia between 2009 and 2019, I offer evidence of mass-media agenda-setting and distraction. First, applying the differences-in-differences technique, I show that state-enforced change in news management induced the previously independent network RBC to increase its reporting about Ukraine. Second, I demonstrate that on nine national media outlets, less reporting about domestic events was associated with transmitting more news reports about Ukraine rather than in other countries. The data set used in this article covers most of the popular domestic television news flows during the period.

Key words: autocratic resilience, Russia, Ukraine, television, news

How do news manipulations in an autocracy vary across different types of mass media? I hypothesise that state-controlled news published online, for consumption by a digitally literate audience, exhibits less pro-regime bias than programming created for those who rely on national television to learn about daily affairs. I test this hypothesis using headlines displayed on the Yandex News Aggregator, transcripts from top three popular state-controlled television channels Channel One, Russia-1, and NTV, and reports from a private news agency Interfax. Using a dictionary technique, I analyse 618,570 news reports which represent almost all popular news flow in Russia in 2019. Results indicate that state-controlled online platform, in contrast to television, demonstrated less tendency to censor stories about political opposition and anti-government protests, but promoted stronger measurable bias in the coverage of news from Ukraine. These findings contribute to the literature on political communication in autocracies and autocratic resilience.

 Key words: autocracy, media bias, news aggregators, Russia

Women have a distinctive voice in parliaments. Analysis of female legislators’ speaking behavior, including floor apportionment and policy issues they address, informs inferences about their effectiveness. In this vein, less adversarial tenor of female MPs’ speeches has been taken as an indicator of their less robust questioning of the members of the executive. To advance our understanding of the gendered patterns of legislative behaviour and their implications, we draw on the scholarship on gender and language in the field of social psychology and scrutinize the legislators’ speaking style. We analyze the text corpus comprised of parliamentary questions and answer sequences in the Croatian parliament from 2004 and 2020, and conduct quantitative content analysis that combines computer-assisted techniques and human coding of the discourse. A novel assessment of how female legislators’ interrogate ministers, rooted in Conversation Analysis―an approach to the study of interactive features of talk―systematically captures linguistic, deliberative and semantic features of discourse. The results show that female parliamentarians are no less robust questioners than their male counterparts. They put pressure on the answerer to account for government policy using different linguistic forms, rather than open aggression evident in semantic features of a speaker’s language. This research furthers the study of gender and politics by demonstrating that the masculine discourse in parliaments is used not only to ensure male dominance in parliaments, as scholars of feminist institutionalism have argued, but also to obscure the agency of female MPs and their effective oversight of the executive.

Key words: gender, legislature, content analysis

Publicness is consequential for transitional justice, but little understood. It can aid or undermine transitional justice depending on the context of redress for past abuse. We advance the study of publicness by conducting a comparative analysis of 671 oral and written questions about transitional justice in the Croatian parliament (2004-2018). Drawing on theory of democratic deliberation , we hypothesize that publicness comes at a cost as it encourages instrumental use of parliamentary questions for legislators’ personal ends. Our evidence shows legislators’ use of oral questions for nationalist grandstanding and of written questions for substantive policy deliberation.

A methods paper about an improved version of Newsmap 

Newsmap is a semi-supervised Naive Bayes-based model for geographical document classification. While fully supervised models are trained on manually classified data, Newsmap learns from “seed words” in toponyms dictionaries which helps classify large data sets without relying on human coders. We offer a new, entropy-based approach which helps achieve higher classification accuracy and precision.

Key words: news, semi-supervised learning, text classifiers