This dissertation aims to analyse media discourse about immigration in Sweden in the last decade. To meet this goal, it uses large-scale textual data collected from various media resources, such as mainstream newspapers, social media (Twitter and Facebook) and an online forum. On the one hand, the dissertation explores how the internal architecture of online media contributes to the formulation of public debate about immigration. On the other hand, this work focuses on an external event represented by the refugee crisis and on the ways in which it intervened with the overall discourse dynamics in the Swedish media. Ultimately, this research aims to understand how these internal and external factors affect the framing and construction of the immigration agenda in Sweden. The methodological framework of the dissertation includes a variety of computational text analysis methods, such as sentiment analysis, topic modelling, word embeddings and machine learning, which helps to gain insight into the content and sentiments of the documents published in the media resources. Text analytic methods are further complemented with social network analysis and the study of communication patterns among social media users.

The main results of the analysis indicate that the refugee crisis played an ambivalent role in the overall dynamics of the immigration discourse. While the analysis results suggest several changes in the interpretative repertoires and sentiment of the media content during the crisis,  it is still questionable if they can be characterised as unique or groundbreaking. As for online social media, this work concludes that they have an ambiguous role in the shaping of public debate on immigration. In particular, the discourse on immigration on social media can be characterised as more negative and prone to the influence of such external events as the refugee crisis. At the same time, even minor changes in the platform architecture can indeed influence the ways in which the immigration discourse is formulated on social media. On the other hand, some of the networked properties of social media, such as clustering or homophily, do not necessarily have a negative or polarising effect, contrary to the predictions of network theory.