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Tuned to Fear : Analyzing Viktor Orbán’s State of the Nation Addresses, focusing on the enemy images identified in the National Consultation

Szalay, Áron and Rakovics, Zsófia (2025) Tuned to Fear : Analyzing Viktor Orbán’s State of the Nation Addresses, focusing on the enemy images identified in the National Consultation. INTERSECTIONS: EAST EUROPEAN JOURNAL OF SOCIETY AND POLITICS, 11 (1). pp. 165-184. ISSN 2416-089X

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Abstract

This study investigates the prevalent use of fear as a political tool by Hungarian Prime Minister Viktor Orbán. Through critical discourse analysis and automated text analytics, we analyze the State of the Nation addresses from 2010 to 2022, identifying recurring enemy images such as ‘Brussels’, ‘migrants’, ‘George Soros’, ‘Ferenc Gyurcsány’ and ‘opposition.’ National Consultations are also examined to understand fear-inducing strategies. Our findings reveal a consistent pattern of fearmongering, with enemy images aligning with key events and political contexts. By employing both qualitative and quantitative methods, this research deepens our understanding of fear’s role in shaping public perceptions and political dynamics. It sheds light on the strategic use of fear in Hungarian politics and its impact on democratic processes. Furthermore, the study highlights the implications for the construction of illiberal systems in Central and Eastern Europe. The analysis of Orbán’s discourse provides valuable insights into the manipulation of public sentiment and the consolidation of power through fear tactics. This study contributes to the broader discourse on the intersections of fear and politics, providing insight into fearmongering through official speeches.

Item Type: Article
Uncontrolled Keywords: fear; political communication; Viktor Orban; automated text analysis; state of nation address; enemy image;
Subjects: J Political Science / politológia > JA Political science (General) / politológia általában
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 15 Feb 2026 21:28
Last Modified: 15 Feb 2026 21:28
URI: https://real.mtak.hu/id/eprint/234114

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