Ph.D. Candidate, Political Science, University of North Carolina at Chapel Hill

Hi!

My research answers how machine learning can be applied to explain and predict political behavior at scale. I focus on developing classification tools that combine experimentation, eye-tracking, and deep learning to understand how stable societal divisions influence political decisions. My work seeks to expand our understanding of these dynamics and improve predictive accuracy in political science, with a primary focus on the U.S. and post-Soviet space.

I prefer to think that my research mainly focuses on one or several of the following fields:

Computational Political Behavior (CPB)
Political Communication (PC)
American Politics (AP)

I plan/hope [insert degree of uncertainty here] to be on the job market in 2025-26.

I am currently working on:

Vision and Division: Democrats and Republicans Look at Political Images Differently, Reflecting Their Attitude. (AP) (CPB) (JMP-2)

In a large-scale eye-tracking study, we show how motivated visual processing reinforces and potentially deepens partisan divides.

Media Choice and Audience Perceptions: Evidence from Visual Framing of Immigration in News Stories. (with Olga Gasparyan) (AP) (PC) (CPB)

We explore how media outlets from different ideological perspectives visually frame a polarizing issue, immigration, and how their target audiences are likely to interpret these frames. We illustrate yet again that media bias is important to study, but this time from the perspective of a visual slant.

When Picture is not Complete: Decoding Visual Sentiment of Political Imagery. (AP) (CPB)

We highlight a key limitation in studies that use large-scale approaches to tackle current challenges in visual sentiment analysis. In addition to demonstrating that certain biases cannot be fully addressed, we offer a guide on how to move forward.

A Theory of Guided Propaganda (CPB) (PC) (JMP-1)

By using machine learning tools, I am exploring persistent structural properties of political propaganda canvas that (could) affect persuasion in autocracy. The project is in the data mining phase.

Autocrat versus Challenger: Revealing Hidden Discontent through Implicit Political Attitudes.

To explain why citizens support or oppose dictatorial leadership, most approaches to studying political behavior and public opinion in autocracies focus on mass explicit preferences. I show that implicit preferences - people's gut attitudes about politics - represent a significant and yet understudied source of citizens' disruptive preferences against the regime and in support of dictators' opponents.

The rest is in my CV.

Publications

Articles

How Biased Media Generate Support for the Ruling Authorities: Causal Mediation Analysis of Evidence from Russia. European Journal of Communication. 2020. Vol.36. N.2. P.183-200. (PC) (CPP)
When the Party’s Over: Political Blame Attribution under an Electoral Authoritarian Regime. Post-Soviet Affairs. 2020. Vol.36. N.1. P.36-60. (with Margarita Zavadskaya) (CPP)
A Top-down Movement with Grass-Roots Effects? Alexei Navalny’s Electoral Campaign. Social Movement Studies. 2018. Vol.17. N.5. P.618-625. (with Jan Dollbaum and Andrei Semenov) (CPP)

Book chapters

Active Urbanites in an Authoritarian Regime. In: Regina Smyth, Jeremy Morris, Andrei Semenov (eds.): Urban Activism in Contemporary Russia, Bloomington: Indiana University Press. (with Jan Dollbaum and Andrei Semenov), 2023. (CPP)
Varieties of Russian Activism: State-society Contestation in Everyday Life. Katie Stewart, Madeline McCann, Carola Neugebauer, Irina Shevtsova, Daniela Zupan, Irina Meyer-Olimpieva, Katherine Hitchcock, John P Burgess, Anna Zhelnina, Anna A Dekalchuk, Ivan S Grigoriev, Eleonora Minaeva, Jan Matti Dollbaum, Guzel Yusupova, Elena Sirotkina. Indiana University Press, 2023. (CPP)