The rise of right-wing populists in Western Europe has often been linked to their ability to exploit social media affordances to fuel anger. While scholarship has already examined the emotional dimension of the populist right’s online communication, with some researchers studying specifically the fuelling of anger among social media users, we still lack empirical proof of the mobilizational effectiveness of what we describe as “anger-triggering communication.” To explore this question, in this article, we develop a statistical and topic analysis of right-wing populists’ Facebook pages in Italy, France, Spain, and Germany during the 2019 European Union (EU) election campaign. We find that (a) right-wing populists on Facebook have a significantly higher number of “Angry” Facebook reactions per post compared to their political adversaries; (b) there is a positive and significant effect of the number of Angry reactions on the number of times a post is shared; (c) Angry reactions and Shares are overrepresented in posts on immigration and security, but anger-fuelled mobilization is not limited to these topics. These findings contribute to the scholarship on social media, emotional communication, and populism, adding insights on the mobilizational effectiveness of negative campaigning. The article highlights that stoking public anger, especially around controversial issues such as immigration and security, is a rewarding tactic because it increases motivational strength, and contributes to triggering high-threshold interactions such as sharing, which, in turn, are key for achieving virality in the diffusion of political messages.

The digital era and the boom of social, user-generated and freely available and usable content on the Net has brought to the fore a classic technique, accused too often of being highly subjective and requiring a large amount of intellectual work. This technique is Content Analysis, which has seen an unprecedented explosion in recent years. In addition to the incessant flow, speed of diffusion and high volume of today’s big data, the attention of social researchers – as well as of anyone interested in drawing information from this enormous proliferation of data – is shifting towards new possibilities. Among these we find that of having a notion of the contents conveyed, of the feelings expressed, of the polarities of big data, but also the chance to extract other information that indirectly speaks of the tastes, opinions, beliefs and transformations behind the behavior of the users of the Net. In fact, secondary data available on the Net, collectable through sophisticated query systems with API or with web scraping software, make it possible to accumulate huge amounts of this dense social data, from which it is possible to try to extract not only trends but real knowledge, in a quantitative as well as in a qualitative manner. This enriches the value of the results that can be produced with Content Analysis and limits, until disappearing, all the critical horizons that have classically left this technique in the shadows, allowing it to find new applicative dignity, validity and reliability (Hamad et al. 2016). In order to explain this evidence, the contribution that we will present attempts to prove that the return of Content Analysis techniques is not only due to the change in the scenario and in the data analyzed, but also to the ability of this technique to innovate and evolve, leading to open analytical perspectives beyond contingent changes. This can be demonstrated through the application of digital mixed content analysis to the recent Covid-19 outbreak and its development of the perception of the Italian population on a specific digital social platform, Twitter. Keywords: Digital Mixed Content Analysis Model, digital platform social data, Twitter, Italy, coronavirus.

The explosion of platform social data as digital secondary data, collectable through sophisticated and automatized query systems or algorithms, makes it possible to accumulate huge amounts of dense and miscellaneous data. The challenge for social researchers becomes how to extract meaning and not only trends in a quantitative and in a qualitative manner. Through the application of a digital mixed content analysis design, we present the potentiality of a hybrid digitalized approach to social content applied to a very tricky question: the recognition of risk perception during the first phase of COVID-19 in the Italian Twittersphere. The contribution of our article to mixed methods research consists in the extension of the existing definitions of content analysis as a mixed approach by combining hermeneutic and automated procedures, and by creating a design model with vast application potential, especially when applied to the digital scenario.

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In the data revolution era, the availability of “voluntary” and “derived from social media” geographic information allowed the spatial dimension to gain attention in digital and web studies. The purpose of this work is to recognize the impact of this research stream on some methodological and theoretical issues. The first regards “critical algorithm studies” in order to understand what algorithms are used. The second concerns how these works conceive the space. The last two issues concern the disciplinary areas in which these researches take place and which are the ecological units taken into account. The authors answer these questions by analyzing, through a content analysis, the researches extracted with the PRISMA methodology that have used Twitter as a data source. The application of this procedure allows the authors to classify the analysis material, moving simultaneously on the four defined dimensions.