Runoff vs. plurality:the effects of the electoral system on local and central government behaviour

Plurality and runoff systems oer very different incentives to parties and coalition of voters, and demand different political strategies from potential candidates and chief executives. Italian mayors and city councils are elected with a different electoral system according to the locality's population, while municipalities are otherwise treated identically in terms of funding and powers. We exploit this institutional feature to test how the presence of different electoral systems affects the central government decisions on grants, and the local government decisions on local taxes. We find evidence that the upper-tier governments favour runoff-elected mayors, and that runoff-elected mayors levy lower taxes. This is broadly consistent with the literature on runoff and plurality rule electoral systems

Dynamics and triggers of misinformation on vaccines

The Covid-19 pandemic has sparked renewed attention on the prevalence of misinformation online, whether intentional or not, underscoring the potential risks posed to individuals' quality of life associated with the dissemination of misconceptions and enduring myths on health-related subjects. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources - both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines. Then, leveraging deep learning models capable to accurately classify vaccine-related content based on the conveyed stance and discussed topic, respectively, we evaluate the focus on various topics by news sources promoting opposing views and compare the resulting user engagement. Aside from providing valuable resources for further investigation of vaccine-related misinformation, particularly in a language (Italian) that receives less attention in scientific research compared to languages like English, our study uncovers misinformation not as a parasite of the news ecosystem that merely opposes the perspectives offered by mainstream media, but as an autonomous force capable of even overwhelming the production of vaccine-related content from the latter. While the pervasiveness of misinformation is evident in the significantly higher engagement of questionable sources compared to reliable ones, our findings underscore the importance of consistent and thorough pro-vax coverage. This is especially crucial in addressing the most sensitive topics where the risk of misinformation spreading and potentially exacerbating negative attitudes toward vaccines among the users involved is higher

Selective Exposure shapes the Facebook News Diet

The social brain hypothesis fixes to 150 the number of social relationships we are able to maintain. Similar cognitive constraints emerge in several aspects of our daily life, from our mobility up to the way we communicate, and might even affect the way we consume information online. Indeed, despite the unprecedented amount of information we can access online, our attention span still remains limited. Furthermore, recent studies showed the tendency of users to ignore dissenting information but to interact with information adhering to their point of view. In this paper, we quantitatively analyze users' attention economy in news consumption on social media by analyzing 14M users interacting with 583 news outlets (pages) on Facebook over a time span of 6 years. In particular, we explore how users distribute their activity across news pages and topics. We find that, independently of their activity, users show the tendency to follow a very limited number of pages. On the other hand, users tend to interact with almost all the topics presented by their favored pages. Finally, we introduce a taxonomy accounting for users behavior to distinguish between patterns of selective exposure and interest. Our findings suggest that segregation of users in echo chambers might be an emerging effect of users' activity on social media and that selective exposure -- i.e. the tendency of users to consume information interest coherent with their preferences -- could be a major driver in their consumption patterns.Comment: PLOS One Published: March 13, 202

News ecosystem dynamics: Supply, Demand, Diffusion, and the role of Disinformation

The digital age provides new challenges as information travels more quickly in a system of increasing complexity. But it also offers new opportunities, as we can track and study the system more efficiently. Several studies individually addressed different digital tracks, focusing on specific aspects like disinformation production or content-sharing dynamics. In this work, we propose to study the news ecosystem as an information market by analysing three main metrics: Supply, Demand, and Diffusion of information. Working on a dataset relative to Italy from December 2019 to August 2020, we validate the choice of the metrics, proving their static and dynamic relations, and their potential in describing the whole system. We demonstrate that these metrics have specific equilibrium relative levels. We reveal the strategic role of Demand in leading a non-trivial network of causal relations. We show how disinformation news Supply and Diffusion seem to cluster among different social media platforms. Disinformation also appears to be closer to information Demand than the general news Supply and Diffusion, implying a potential danger to the health of the public debate. Finally, we prove that the share of disinformation in the Supply and Diffusion of news has a significant linear relation with the gap between Demand and Supply/Diffusion of news from all sources. This finding allows for a real-time assessment of disinformation share in the system. It also gives a glimpse of the potential future developments in the modelisation of the news ecosystem as an information market studied through its main drivers

Cross-platform impact of social media algorithmic adjustments on public discourse

In the hypertrophic and uncharted information world, recommender systems are gatekeepers of knowledge. The evolution of these algorithms is usually an opaque process, but in February 2023, the recommender system of X, formerly Twitter, was altered by its chairman (Elon Musk) transparently, offering a unique study opportunity. This paper analyses the cross-platform impact of adjusting the platform's recommender system on public discourse. We focus on the account of Elon Musk and, for comparison, the account of the President of the United States (Joe Biden). Our results highlight how algorithm adjustments can boost content visibility, user engagement, and community involvement without increasing the engagement or involvement probabilities. We find that higher visibility can increase the influence on social dialogue but also backfire, triggering negative community reactions. Finally, our analysis offers insights to detect future less transparent changes to recommender systems

From Trust to Disagreement: disentangling the interplay of Misinformation and Polarisation in the News Ecosystem

The increasing pervasiveness of fruitless disagreement poses a considerable risk to social cohesion and constructive public discourse. While polarised discussions can exhibit significant distrust in the news, it is still largely unclear whether disagreement is somehow linked to misinformation. In this work, we exploit the results of `Cartesio', an online experiment to rate the trustworthiness of Italian news articles annotated for reliability by expert evaluators. We developed a metric for disagreement that allows for correct comparisons between news with different mean trust values. Our findings indicate that, though misinformation receives lower trust ratings than accurate information, it does not appear to be more controversial. Additionally, we examined the relationship between these findings and Facebook user engagement with news articles. Our results show that disagreement correlates with an increased likelihood of commenting, probably linked to inconclusive and long discussions. The emerging scenario is one in which fighting disinformation seems ineffective in countering polarisation. Disagreement focuses more on the divergence of opinions, trust, and their effects on social cohesion. This study offers a foundation for unsupervised news item analysis independent of expert annotation. Incorporating similar principles into the design of news distribution platforms and social media systems can enhance online interactions and foster the development of a less divisive news ecosystem

Unveiling the hidden agenda: Biases in news reporting and consumption

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 well-being. 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