Current Projects
Fakenews: political news on Russia and its neighbors on social media: major content features, factors of trust and news truthfulness detection by users of different countries (2019-2021) (RSF)
FakeNews project aims to study how people perceive online news and how they orient in a growing sea of virtually unverifiable information; to what degree they are vulnerable to fake news and what influences their ability to detect them. We plan to develop a model of authenticity perception and find factors influencing the ability to detect deception in a situation of varying tension between countries under news coverage.
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Development of concept and methodology for multi-level monitoring of the state of interethnic relationswith the data from social media
The major result of this project is a conceptual approach to and a methodology of system devised to monitor ethnic relations on the Post-Soviet space. Its main goal is to trace, in a semi-automatic way, distribution of discussions about ethnicity in the Russian-language social media over time and space. The primary task of this tracing is early prevention of emerging inter-ethnic conflicts.
Social capital and privacy online: an urban community on a social networking site
Social networking sites (SNS) provide their users with a diverse functionality for communication which s enables them to grow their social capital, but simultaneously poses risks to user privacy. This is especially true given that communication networks, as a rule, develop within the same city or the closest rural area where anonymity is not always possible.
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Socio-political News Spread in Russian Language Online Social Network
The goal of this research is to identify mechanisms that drive dissemination of professionally produced public affairs news across a Russian-language online social network (represented by VKontakte). The research investigates which features of messages, users and network topology influence this process, and which of them contribute to emergence of information cascades. The main anticipated result of this project is a model describing parameters of such cascades and the knowledge about the role of various factors in their generation.
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Improving the Methodology of Automatic Text Analysis
Topic modeling is a promising instrument for computational social science and digital humanities as it allows to automatically reveal thematic structure of large text collections – an immensely important task in the era of big Internet data. However, topic modeling, notably LDA as its main algorithm, has a number of problems that prevent its efficient use by social scientists, including social media analysts.
The Representation of Ukrainian Crisis in News: Frame Analysis with Topic Modelling
Media framing is an important issue in media studies and political science. Media frames have been traditionally extracted via manual content and discourse analysis. Such approach has a limited ability to deal with large text collections and is prone to subjectivity both in terms of text selection and interpretation.
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