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When analyzing digital journalism content, journalism scholars are confronted with a number of substantial differences compared to traditional journalistic content. The sheer amount of data and the unique features of digital content call for the application of valuable new techniques. Various other scholarly fields are already applying computational methods to study digital journalism data. Often, their research interests are closely related to those of journalism scholars. Despite the advantages that computational methods have over traditional content analysis methods, they are not commonplace in digital journalism studies. To increase awareness of what computational methods have to offer, we take stock of the toolkit and show the ways in which computational methods can aid journalism studies. Distinguishing between dictionary-based approaches, supervised machine learning, and unsupervised machine learning, we present a systematic inventory of recent applications both inside as well as outside journalism studies. We conclude with suggestions for how the application of new techniques can be encouraged.

The phenomenon of (media) personalisation has generated a considerable amount of scholarly attention and is regarded as being a significant aspect of contemporary politics and political communication.To investigate long-term trends in personalisation, this study draws on a unique data set that is composed of Dutch and British newspaper articles from a sixteen-year period. The evidence suggests that personalisation is present in both countries, but its realisation differs in each country: where the UK is characterised by a concentration of attention towards the prominent offices and the Prime Minister, the Netherlands has seen an increased visibility of the 'non-prominent' Cabinet members.The second part of the study investigates the impact of various events on the visibility levels of the most prominent ministerial functions, with particular attention paid to the Prime Minister. Leaders appear not to be more in the foreground during national crises. Finally, the explanatory power of events for determining the visibility of Cabinet members does not seem to increase in importance over time.

Populist attributions of blame have important effects on citizens’ attitudes, cognitions, emotions, and behaviors. Extending previous studies that have mostly looked at populist messages blaming political elites, we use an online survey experiment (N = 805) to investigate the effects of blaming different elitist actors in populist and non-populist ways: (1) political elites, (2) corporate elites, (3) scientific elites, and (4) a combination of these elites. We compare mere causal responsibility attribution to populist blame attributions that highlight a central opposition between “pure ordinary people” and “corrupt elites” to tease out the specific effect of populist rhetoric. Results suggest that populist messages blaming all elites have stronger effects than isolated cues. Furthermore, populist blame attribution leads to more negative perceptions of the elites than non-populist blame attribution. The finding that populist frames can – under some conditions – activate negative perceptions toward different elites is alarming in times of declining trust in societal institutions, and calls for a more encompassing understanding of populists’ scapegoats.

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