In this undergraduate thesis I conduct a literature review of 51 peer-reviewed articles published between 2020 and 2026, examining which methodological approaches appear in contemporary research on digital nationalism and what advantages, limitations, and dilemmas they entail. As the theoretical framework for the review I employ Bandara’s typology of five forms of digital nationalism: digital ethno-nationalism, populist nationalism, algorithmic nationalism, cultural and consumer nationalism, and meme nationalism. The review demonstrates that digital ethno-nationalism is the most prevalent form, while geographically the literature is heavily concentrated on China. The choice of methodological approaches is closely tied to the form of digital nationalism under study. Sentiment analysis and topic modelling predominate in research on digital ethno-nationalism, while dictionary and lexicon-based approaches are most common in studies of populist nationalism. Cultural and consumer nationalism is methodologically the most diverse, encompassing qualitative discourse analysis and machine learning, whereas meme nationalism relies primarily on in-depth manual coding and multimodal discourse analysis. From 2024 onwards, the reviewed corpus shows a notable increase in the use of transformer-based models and approaches that combine quantitative computational methods and qualitative methods. Qualitative methods – particularly critical discourse analysis, multimodal discourse analysis, and critical framing analysis – remain indispensable in the study of meme nationalism. The thesis also highlights the language bias of methodological tools in favour of English, the limited empirical coverage of algorithmic nationalism, the specific challenges of studying meme nationalism, and the tension between breadth and depth in mixed-methods research.
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