Media Storms, dramatic outbursts of attention to a story, are central components of media dynamics and the attention landscape. Despite their significance, there has been little systematic and empirical research on this concept due to issues of measurement and operationalization. We introduce an iterative human-in-the-loop method to identify media storms in a large-scale corpus of news articles. The text is first transformed into signals of dispersion based on several textual characteristics. In each iteration, we apply unsupervised anomaly detection to these signals; each anomaly is then validated by an expert to confirm the presence of a storm, and those results are then used to tune the anomaly detection in the next iteration. We demonstrate the applicability of this method in two scenarios: first, supplementing an initial list of media storms within a specific time frame; and second, detecting media storms in new time periods. We make available a media storm dataset compiled using both scenarios. Both the method and dataset offer the basis for comprehensive empirical research into the concept of media storms, including characterizing them and predicting their outbursts and durations, in mainstream media or social media platforms.

Elites forming a perception of what the public wants is an important way in which democratic representation comes about, the assumption holds. Yet very few are the studies that examine the effect of elite perceptions on politician action. This study sets out to revisit the matter, measuring actual public priorities, elite perceptions of public priorities and a wide range of representative actions with regard to a few hundred concrete issues. We find that elite perceptions matter for their representative behavior; elites are much more likely to take action on issues they believe citizens care about. The effect exists across the board; perceptions matter in three different political systems, for different types of political action, and for electorally safe and unsafe, trustee and delegate politicians alike. These results speak to the micro-level factors connecting public and policy agendas, and the conditions under which representatives are attentive to public issue priorities.

Previous research on political representation mainly focused on representatives but has overlooked individuals’ sense of inclusion within the greater group: the nation. Building on narrative theory, we propose a novel mechanism that fosters a feeling of political representation—a similarity between individuals’ personal-national stories and the collective-national metanarrative of the polity. Metanarratives are shared dominant stories that present a community with desirable social conventions. While they seek to represent the nation, metanarratives are not entirely inclusive and do not appeal to all citizens. We argue that individuals who do internalize the metanarrative feel more represented. To demonstrate our theoretical expectations, we use election surveys and constitutive texts to capture national stories and metanarratives. We find that indeed respondents whose national story is closer to a nation’s metanarrative feel more represented by the political system.

As more computational communication researchers turn to supervised machine learning methods for text classification, we note the challenge in implementing such techniques within an imbalanced dataset. Such issues are critical in our domain, where, in many cases, researchers attempt to identify and study theoretically interesting categories that can be rare in a target corpus. Specifically, imbalanced distributions, with a skewed distribution of texts among the categories, can lead to a lengthy and expensive annotation stage, forcing practitioners to sample and label large numbers of texts to train a classification model. In this paper, we provide an overview of the issue, and describe existing strategies for mitigating such challenges. Noting the pitfalls of previous solutions, we then provide a semi-supervised method – Expert Initiated Latent Space Sampling – that complements researcher domain expertise with a systematic, unsupervised exploration of the latent semantic space to overcome such limitations. Utilizing simulations to systematically evaluate our method and compare it to existing approaches, we show that our procedure offers significant advantages in terms of efficiency and accuracy in many classification tasks.

Data suggests that the majority of citizens in various countries came across ‘fake news’ during the COVID-19 pandemic. We test the relationship between perceived prevalence of misinformation and people’s worries about COVID-19. In Study 1, analyses of a survey across 17 countries indicate a positive association: perceptions of high prevalence of misinformation are correlated with high worries about COVID-19. However, the relationship is weaker in countries with higher levels of case-fatality ratios, and independent from the actual amount of misinformation per country. Study 2 replicates the relationship using experimental data. Furthermore, Study 2 demonstrates the underlying mechanism, that is, perceived prevalence of misinformation fosters the belief that COVID-19 is spiralling out of control, which in turn, increases worries. Our findings suggest that perceived prevalence of misinformation can have significant psychological effects, even though audience members reject the information as being false.

In the modern political landscape, the massive use of digital networked technologies, such as websites and social media, holds direct consequences for the relationship between three dominant social actors – the media, politicians, and citizens. While some argue that new media technologies may restore citizens’ capacity to shape their political arena, others emphasize the traditional power of politicians and the media. In light of these competing expectations, this chapter examines issue congruence, or agenda congruence, between these three actors during the four elections that took place during 2019–2021. This chapter integrated a 12-wave panel survey with a computational content analysis of 36 Israeli news websites and 36,169 Facebook posts by 205 politicians. The findings shows that Israeli media environment, which is often blamed as biased toward the political left, emphasized more right-wing issue priorities. Moreover, our findings point to the interdependence between the agendas of journalists and politicians even though the modern digital era enables them to directly engage with the public. Thus, it does not find evidence for scholars’ optimistic views of digital networked technologies that can empower citizens at the expense of political elites.

The 2019–2021 election cycle was focused on Netanyahu, the incumbent prime minister, for over a decade. Second on these elections’ agenda was the future of the Israeli democracy. Based on the literature of political personalization, this chapter examines whether and how Netanyahu has embodied attitudes toward democratic principles and values in this election cycle, calling this understudied manifestation of centralized personalization “personification.” The findings indeed indicate personification, beyond already-documented political and sociological sources of democratic support. Netanyahu personified authoritarian views as well as illiberal approaches. This chapter suggests that personification may be a mechanism of democratic erosion, thus joining the growing public and academic concerns about democratic backsliding in Israel and worldwide.

Modern democracies are built around the idea of representation: the electorate is represented, and perhaps more importantly, should feel represented. Yet recent decades have been marked by a growing gap between citizens and the political system. The emergence of new, more direct communication channels such as social media gave rise to expectations that the two sides can be brought closer together. This study investigates whether a connection with politicians and parties via social media platforms can enhance citizens’ feelings of being represented by a certain politician or party (dyadic representation), or by the entire parliament (collective representation). Our data come from a two-year panel study, covering four Israeli elections. Findings demonstrate that passive (receiving political information), but not active (producing political information) connection increases dyadic representation perceptions. Collective representation perceptions, by contrast, were not affected by either type of connection. Furthermore, our analysis does not demonstrate a reverse causal order; we did not find evidence supporting the idea that representation perceptions influence connection with politicians or parties. The upshot of our findings is that unidirectional communication between politicians and citizens on social media can foster citizens’ feelings of being represented in the political arena and of closeness with the political elites. That said, such effects were obtained only with regard to dyadic representation and are therefore limited in scope. The study contributes to the ongoing theoretical debate concerning the complex relations between new technologies, citizens, and political elites.

A widely believed claim is that citizens tend to selectively expose themselves to like-minded information. However, when individuals find the information useful, they are more likely to consume cross-cutting sources. While crises such as terror attacks and pandemics can enhance the utility of cross-cutting information, empirical evidence on the role of real-world external threats in selective exposure is scarce. This paper examines the COVID-19 pandemic as a case study to test the extent to which citizens were exposed to information from cross-cutting sources on traditional and social media after the outbreak. Utilizing a two-wave panel survey among 14,218 participants across 17 countries – conducted before and after the initial outbreak – we show that citizens concerned about COVID-19 were more exposed to cross-cutting information on traditional and social media. The positive relationship with cross-cutting exposure to traditional news was stronger in countries where governments adopted less stringent policy responses, and in countries with greater pandemic severity and weaker democratic institutions. Our comparative approach thus sheds light on the social and political contexts in which cross-cutting exposure can occur.

Many scholars stress the role national identities play as an essential element that shapes interests and explains political behaviours. Others, however, contend that national identities are too amorphic and highlight the analytical challenge of employing them as a research variable. We propose the use of metanarratives as a theoretical framework that captures the essence of national identities and allows the comparative study of their similarities and differences. Metanarratives are shared dominant stories that guide values, beliefs and behaviours and help communities understand who they are. We develop a new systematic method for measuring their content and present a three-step process for gauging metanarratives. We demonstrate this method on 159 countries, analysing constitution preambles to assess each nation's metanarrative and create a global identity orientation map. We show how this approach enables the classification and comparison of national identities and discuss its potential contribution to further empiric study of national identities.