In an explorative approach, this thesis draws upon the benefits of using Artificial Intelligence (AI) for analysing text with Topic Modelling in an attempt to measure populism in Swedish news. This project breaks new ground in the field of media and communication studies by including 15 200 000 words from Swedish news articles published between 2012 and 2022 and steps into the next generation of news analysis that incorporates data driven methods to unload the burden of quantitative content analysis. By extracting the most salient aspects of populism and feeding them to the Top2Vec algorithm, keywords related to populism is measured over time and space and a new value describing to what degree news agencies is complicit in media populism is developed. Some of the most noticeable findings include, identifying keywords related to populism, Aftonbladet’s elevated degree of media populism and that the focus of Swedish media populism shifts over time, from the “People” to the “Anti-Elite” aspects of populism.