Suchergebnisse

The contemporary media systems present hybrid logics and features that imply an increasingly interdependence among actors, media and communication formats. The hybrid media system approach underlines that legacy news media and non-elite media actors construct flows of news through different media technologies and according to complex temporal structures. A media environment arises in which traditional distinctions between concepts like 'online' and 'offline', 'producer' and 'audience', 'citizens' and 'journalists' become blurred. The emphasis appears to be on change more than continuity, and on difference more than similarity. Although the hybrid media approach is appreciated by numerous contemporary media scholars, hybridity in media often remains an all-encompassing concept and few attempts have been made to measure it. This article assesses the level of hybridity by investigating journalists' uses of sources. It considers mainly journalists' use of sources by the medium for which they work (from newspapers to web or radio) and the kinds of news that they produce (hard or soft news, business/finance, tech/science). The assumption is, therefore, that, within a homogeneously hybridized media system, journalists use the same sources regardless of the medium for which they work and the topics with which they deal. This objective is pursued by analysing the data collected via a survey conducted by means of structured interviews with a sample of 1424 Italian journalists between October and November 2016. The findings show that the analytical distinction among platforms for which journalists work still matters in terms of sources. Except for the use of Facebook and Google, journalists have still very defined paths to collect sources according to the medium they work for. The article has implications also for the literature on journalists' authority and expertise.

The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. In this setting, we aim at assessing gender gaps in the mediated personalization of a wide range of political office holders in Italy during the period 2017–2020 by means of a combination of NLP and statistical methods. The proposed analysis hinges on the definition of a new score for each word in the corpus that adjusts the incidence rate for the under representation of women in politics. On this basis, evidence is found that political personalization in Italy is more detrimental for women than it is for men, with the persistence of entrenched stereotypes including a masculine connotation of leadership, the resulting women's unsuitability to hold political functions, and a greater deal of focus on their attractiveness and body parts. In addition, women politicians are covered with a more negative tone than their men counterpart when personal details are reported. By distinguishing between different types of media, we also show that the observed gender differences are primarily found in online news rather than print news. This suggests that the expression of certain stereotypes may be favored when click baiting and personal targeting have a major impact.

Looking at TV news viewership in Italy during the COVID-19 pandemic, we investigate whether demand for national and local news depends on national or local events. Exploiting the fact that epidemiological developments display a great deal of variation among the different regions, we find that demand for both national news and, more surprisingly, local news responds to the national epidemiological developments rather than to the local ones. This implies that local politicians have a further incentive to take preventive action.

Looking at TV news viewership in Italy during the COVID-19 pandemic using actual consumption data, we investigate whether demand for national and local news depends on national or local epidemiological developments, as measured by the number of new positives or the number of currently positives in any given day. Exploiting the fact that the seriousness of the pandemic displays a great deal of variation among the different regions, we find that at the regional level demand for both national news and, more surprisingly, local news responds to the national epidemiological developments rather than to the local ones. This has implications for the incentives faced by local politicians to take preventive action.

Abstract
Recognizing the presence and impact of news outlets' biases on public discourse is a crucial challenge. Biased news significantly shapes how individuals perceive events, potentially jeopardizing public and individual wellbeing. In assessing news outlet reliability, the focus has predominantly centered on narrative bias, sidelining other biases such as selecting events favoring specific perspectives (selection bias). Leveraging machine learning techniques, we have compiled a six-year dataset of articles related to vaccines, categorizing them based on narrative and event types. Employing a Bayesian latent space model, we quantify both selection and narrative biases in news outlets. Results show third-party assessments align with narrative bias but struggle to identify selection bias accurately. Moreover, extreme and negative perspectives attract more attention, and consumption analysis unveils shared audiences among ideologically similar outlets, suggesting an echo chamber structure. Quantifying news outlets' selection bias is crucial for ensuring a comprehensive representation of global events in online debates.