News content has a powerful impact on society and politics. Nowadays, a vast amount of information is available through the internet, which could reduce its transparency and increase the opportunity for news slant. People consume a lot of different kinds of media and rely on them as news source while often simply assuming that it is reliable. Slant is defined as the more or less favourable news coverage of an individual or group, which can be due to reality and bias. Slant can be explicit if people are aware of it, but implicit or unconscious if it is something they don’t realize. People have to recognize slants to tackle them fully. News slants can affect the selection of events and stories published, the perspective from which they are written, and can affect newspaper readers. We hypothesize that these slants manifest not only in what news describes but where they place emphasis, how they frame events, and what they keep silent about.

News

Our project was one of the organizers of the Budapest Methods Workshop, held with great success in Budapest. The event brought together over 50 international participants, including many young scholars.

Several members of our project team delivered presentations:

  • Jakub Stauber: The Narratives of the War in Ukraine in Czech News Media
  • Krzysztof Rybinski: Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks

Events

On October 17, 2024, Jakub Stauber, guest speaker gave a lecture at the HUN-REN Centre for Social Sciences Institute for Political Science titled The Narratives of the War in Ukraine in Czech News Media.

Jakub Stauber is an assistant professor at the Institute of Political Science of Charles University in Prague. The guest lecture focused on applying supervised machine-learning methods to detect the presence of Russian and Western narratives about the war in Ukraine. Based on a unique corpus of Czech main news media outlets, the presented analysis demonstrated the capabilities and possible limitations of the newly fine-tuned deBERTa model.

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