To understand public opinion about immigration in Europe, one has to understand the media's role in it. We present a literature review on research on media discourse on immigration and their effects. Despite differences in the way immigration and migrant groups are represented in European media, we can observe common patterns. Migrants are generally under-represented and shown as delinquents or criminals. Although, media framing differs based on specific migrant groups the discourse is focusing on, immigration coverage is often negative and conflict-centred. Frequent exposure to such media messages leads to negative attitudes towards migration, may activate stereotypical cognitions of migrant groups, and even influence vote choice. In addition to discussing these issues in depth, the present review also focuses on comparative findings.

The complexity and duration of the so-called ‘European refugee crisis’ created a climate of uncertainty, which left ample room for mass media to shape citizens’ understanding of what the arrival of these refugees meant for their respective country. This study analyses the national media discourses in Hungary, Germany, Sweden, the United Kingdom and Spain for this time period. Applying Latent Dirichlet Allocation topic modelling in five languages and based on N = 130,042 articles from 24 news outlets, we reveal country-specific media frames to track the overall course of the refugee debate and to uncover dynamics and shifts in discourses. While results show similarities across countries, due to media coverage responding to real-world developments, there are differences in media framing as well. Possible sources of these differences such as countries’ geographic location or status as receiving country are discussed.

We investigate the effects of sentiment and issue salience on emotionally labeled responses to posts from political actors on Facebook (i.e., Reactions). We use an automated content analysis of Facebook posts and voter survey data in a multilevel negative binomial regression approach. Findings show that the sentiment of a post relates to the number of "Love" and "Angry" Reactions. Furthermore, if a post addresses an issue that constituents perceive as salient, this positively influences the number of "Angry" Reactions only. We also find that the effect of sentiment on "Angry" Reactions is highest when issue salience is low.

Political communication has become one of the central arenas of innovation in the application of automated analysis approaches to ever-growing quantities of digitized texts. However, although researchers routinely and conveniently resort to certain forms of human coding to validate the results derived from automated procedures, in practice the actual "quality assurance" of such a "gold standard" often goes unchecked. Contemporary practices of validation via manual annotations are far from being acknowledged as best practices in the literature, and the reporting and interpretation of validation procedures differ greatly. We systematically assess the connection between the quality of human judgment in manual annotations and the relative performance evaluations of automated procedures against true standards by relying on large-scale Monte Carlo simulations. The results from the simulations confirm that there is a substantially greater risk of a researcher reaching an incorrect conclusion regarding the performance of automated procedures when the quality of manual annotations used for validation is not properly ensured. Our contribution should therefore be regarded as a call for the systematic application of high-quality manual validation materials in any political communication study, drawing on automated text analysis procedures.

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