This study undertakes a systematic analysis of media discourse on migration in Sweden from 2012 to 2019. Using a novel data set consisting of mainstream newspapers, Twitter and forum data, the study answers two questions: What do Swedish media actually talk about when they talk about “migration”? And how do they talk about it? Using a combination of computational text analysis tools, I analyze a shift in the media discourse seen as one of the outcomes of the European refugee crisis in 2015 and try to understand the role of social media in this process. The results of the study indicate that messages on social media generally had negative tonality and suggest that some of the media frames can be attributed to a migration-hostile discourse. At the same time, the analysis of framing and sentiment dynamics provides little evidence for the discourse shift and any long-term effects of the European refugee crisis on the Swedish media discourse. Rather, one can hypothesize that the role of the crisis should be viewed in a broader political and historical context.

This work aims to study the construction of migrant categories and immigration discourse on Swedish-speaking Facebook pages in the last decade. It combines the insights from computational linguistics and distributional semantics approach with those from discursive psychology to explore a corpus of more than 1 M Facebook posts. This allows one to compare the meanings of labels denoting various categories of migrants and identify the key interpretative repertoires used to discuss the immigration topic. The study finds that the ‘immigrant’ category has stronger association with potential costs, benefits and threat to the host society, while the ‘refugee’ category is presented as in need of support and solidarity. Nevertheless, objectification and exclusionary rhetoric are used in relation to both categories, although in different ways, while the immigration issue is often interpreted as a matter of Sweden’s national concern rather than as a part of people’s actual experiences and life paths.

This paper presents a study on the dynamics of sentiment polarisation in the active online discussion communities formed around a controversial topic—immigration. Using a collection of tweets in the Swedish language from 2012 to 2019, we track the development of the communities and their sentiment polarisation trajectories over time and in the context of an exogenous shock represented by the European refugee crisis in 2015. To achieve the goal of the study, we apply methods of network and sentiment analysis to map users’ interactions in the network communities and quantify users’ sentiment polarities. The results of the analysis give little evidence for users’ polarisation in the network and its communities, as well as suggest that the crisis had a limited effect on the polarisation dynamics on this social media platform. Yet, we notice a shift towards more negative tonality of users’ sentiments after the crisis and discuss possible explanations for the above-mentioned observations.

Humans behavior often varies depending on the opponent’s group membership, with both positive consequences (e.g., cooperation or mutual help) and negative ones (e.g., stereotyping, oppression, or even genocide). An influential model developed by Hammond and Axelrod (HA) highlighted the emergence of macrolevel “ethnocentric cooperation” from the aggregation of microlevel interactions based on arbitrary tags signaling group membership. We extended this model to include a wider set of agents’ behaviors including the possibility of harming others. This allowed to check whether and under which conditions xenophobia can emerge beside or in alternative to ethnocentric cooperation. The model was compared to Swedish data documenting social unrest and proxies of cooperative behaviors at the municipal level. The validation results supported the model predictions on conflict but not the ones on cooperation, casting doubts on HA’s original argument.

This study makes a systematic analysis of media discourse on migration in Sweden in 2012-2019. Using a novel dataset consisting of mainstream newspapers, Twitter and forum data, the article answers two questions: what do Swedish media actually talk about when they talk about “migration”? And how do they talk about it? Using a combination of computational text analysis tools, I analyse a shift in the media discourse as one of the outcomes of the so-called “European refugee crisis” in 2015 and try to comprehend the role of social media in this process. The results of the study provide some evidence for a populist and hostile character of the migration framing and indicate that the messages on social media, generally, had negative tonality. At the same time, the analysis of framing and sentiment dynamics suggests that a statement about the discourse shift in the Swedish media during the “European refugee crisis” is to some extent over-estimated. Rather, one can suggest that the role of the “crisis” should be viewed in a broader political and historical context.

In this work, we explore the performance of supervised stance classification methods for social media texts in under-resourced languages and using limited amounts of labeled data. In particular, we focus specifically on the possibilities and limitations of the application of classic machine learning versus deep learning in social sciences. To achieve this goal, we use a training dataset of 5.7K messages posted on Flashback Forum, a Swedish discussion platform, further supplemented with the previously published ABSAbank-Imm annotated dataset, and evaluate the performance of various model parameters and configurations to achieve the best training results given the character of the data. Our experiments indicate that classic machine learning models achieve results that are on par or even outperform those of neural networks and, thus, could be given priority when considering machine learning approaches for similar knowledge domains, tasks, and data. At the same time, the modern pre-trained language models provide useful and convenient pipelines for obtaining vectorized data representations that can be combined with classic machine learning algorithms. We discuss the implications of their use in such scenarios and outline the directions for further research.

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The goal of this work is to study the social construction of migrant categories and immigration discourse on Swedish Facebook in the last decade. I combine the insights from computational linguistics and distributional semantics approach with those from classical sociological theories in order to explore a corpus of more than 1M Facebook posts. This allows to compare the intended meanings of various linguistic labels denoting voluntary versus forced character of migration, as well as to distinguish the most salient themes that constitute the Facebook discourse. The study concludes that, although Facebook seems to have the highest potential in the promotion of tolerance and support for migrants, its audience is nevertheless active in the discursive discrimination of those identified as "refugees" or "immigrants". The results of the study are then related to the technological design of new media and the overall social and political climate surrounding the Swedish immigration agenda.