News and misinformation consumption: A temporal comparison across European countries.

The Internet and social media have transformed the information landscape, democratizing content access and production. While making information easily accessible, these platforms can also act as channels for spreading misinformation, posing crucial societal challenges. To address this, understanding news consumption patterns and unraveling the complexities of the online information environment are essential. Previous studies highlight polarization and misinformation in online discussions, but many focus on specific topics or contexts, often overlooking comprehensive cross-country and cross-topic analyses. However, the dynamics of debates, misinformation prevalence, and the efficacy of countermeasures are intrinsically tied to socio-cultural contexts. This work aims to bridge this gap by exploring information consumption patterns across four European countries over three years. Analyzing the Twitter activity of news outlets in France, Germany, Italy, and the UK, this study seeks to shed light on how topics of European significance resonate across these nations and the role played by misinformation sources. The results spotlight that while reliable sources predominantly shape the information landscape, unreliable content persists across all countries and topics. Though most users favor trustworthy sources, a small percentage predominantly consumes content from questionable sources, with even fewer maintaining a mixed information diet. The cross-country comparison unravels disparities in audience overlap among news sources, the prevalence of misinformation, and the proportion of users relying on questionable sources. Such distinctions surface not only across countries but also within various topics. These insights underscore the pressing need for tailored studies, crucial in designing targeted and effective countermeasures against misinformation and extreme polarization in the digital space.

Cross-platform social dynamics: an analysis of ChatGPT and COVID-19 vaccine conversations.

The role of social media in information dissemination and agenda-setting has significantly expanded in recent years. By offering real-time interactions, online platforms have become invaluable tools for studying societal responses to significant events as they unfold. However, online reactions to external developments are influenced by various factors, including the nature of the event and the online environment. This study examines the dynamics of public discourse on digital platforms to shed light on this issue. We analyzed over 12 million posts and news articles related to two significant events: the release of ChatGPT in 2022 and the global discussions about COVID-19 vaccines in 2021. Data was collected from multiple platforms, including Twitter, Facebook, Instagram, Reddit, YouTube, and GDELT. We employed topic modeling techniques to uncover the distinct thematic emphases on each platform, which reflect their specific features and target audiences. Additionally, sentiment analysis revealed various public perceptions regarding the topics studied. Lastly, we compared the evolution of engagement across platforms, unveiling unique patterns for the same topic. Notably, discussions about COVID-19 vaccines spread more rapidly due to the immediacy of the subject, while discussions about ChatGPT, despite its technological importance, propagated more gradually.

Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections.

Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter's news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers-users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.

Conspiracy theories and social media platforms.

Conspiracy theories proliferate online. We provide an overview of information consumption patterns related to conspiracy content on four mainstream social media platforms (Facebook, Twitter, YouTube, and Reddit), with a focus on niche ones. Opinion polarisation and echo chambers appear as pivotal elements of communication around conspiracy theories. A relevant role may also be played by the content moderation policies enforced by each social media platform. Banning contents or users from a social media could lead to a level of user segregation that goes beyond echo chambers and reaches the entire social media space, up to the formation of 'echo platforms'. The insurgence of echo platforms is a new online phenomenon that needs to be investigated as it could foster many dangerous phenomena that we observe online, including the spreading of conspiracy theories.

COVID-19 infodemic on Facebook and containment measures in Italy, United Kingdom and New Zealand.

The COVID-19 pandemic has been characterized by a social media "infodemic": an overabundance of information whose authenticity may not always be guaranteed. With the potential to lead individuals to harmful decisions for the society, this infodemic represents a severe threat to information security, public health and democracy. In this paper, we assess the interplay between the infodemic and specific aspects of the pandemic, such as the number of cases, the strictness of containment measures, and the news media coverage. We perform a comparative study on three countries that employed different managements of the COVID-19 pandemic in 2020-namely Italy, the United Kingdom, and New Zealand. We first analyze the three countries from an epidemiological perspective to characterize the impact of the pandemic and the strictness of the restrictions adopted. Then, we collect a total of 6 million posts from Facebook to describe user news consumption behaviors with respect to the reliability of such posts. Finally, we quantify the relationship between the number of posts published in each of the three countries and the number of confirmed cases, the strictness of the restrictions adopted, and the online news media coverage about the pandemic. Our results show that posts referring to reliable sources are consistently predominant in the news circulation, and that users engage more with reliable posts rather than with posts referring to questionable sources. Furthermore, our modelling results suggest that factors related to the epidemiological and informational ecosystems can serve as proxies to assess the evolution of the infodemic.

Entropy and complexity unveil the landscape of memes evolution.

On the Internet, information circulates fast and widely, and the form of content adapts to comply with users' cognitive abilities. Memes are an emerging aspect of the internet system of signification, and their visual schemes evolve by adapting to a heterogeneous context. A fundamental question is whether they present culturally and temporally transcendent characteristics in their organizing principles. In this work, we study the evolution of 2 million visual memes published on Reddit over ten years, from 2011 to 2020, in terms of their statistical complexity and entropy. A combination of a deep neural network and a clustering algorithm is used to group memes according to the underlying templates. The grouping of memes is the cornerstone to trace the growth curve of these objects. We observe an exponential growth of the number of new created templates with a doubling time of approximately 6 months, and find that long-lasting templates are associated with strong early adoption. Notably, the creation of new memes is accompanied with an increased visual complexity of memes content, in a continuous effort to represent social trends and attitudes, that parallels a trend observed also in painting art.

Human mobility in response to COVID-19 in France, Italy and UK.

The COVID-19 pandemic is one of the defining events of our time. National Governments responded to the global crisis by implementing mobility restrictions to slow down the spread of the virus. To assess the impact of those policies on human mobility, we perform a massive comparative analysis on geolocalized data from 13 M Facebook users in France, Italy, and the UK. We find that lockdown generally affects national mobility efficiency and smallworldness-i.e., a substantial reduction of long-range connections in favor of local paths. The impact, however, differs among nations according to their mobility infrastructure. We find that mobility is more concentrated in France and UK and more distributed in Italy. In this paper we provide a framework to quantify the substantial impact of the mobility restrictions. We introduce a percolation model mimicking mobility network disruption and find that node persistence in the percolation process is significantly correlated with the economic and demographic characteristics of countries: areas showing higher resilience to mobility disruptions are those where Value Added per Capita and Population Density are high. Our methods and findings provide important insights to enhance preparedness for global critical events and to incorporate resilience as a relevant dimension to estimate the socio-economic consequences of mobility restriction policies.

The echo chamber effect on social media.

Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers' formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.

The COVID-19 social media infodemic.

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

The limited reach of fake news on Twitter during 2019 European elections.

The advent of social media changed the way we consume content, favoring a disintermediated access to, and production of information. This scenario has been matter of critical discussion about its impact on society, magnified in the case of the Arab Springs or heavily criticized during Brexit and the 2016 U.S. elections. In this work we explore information consumption on Twitter during the 2019 European Parliament electoral campaign by analyzing the interaction patterns of official news outlets, disinformation outlets, politicians, people from the showbiz and many others. We extensively explore interactions among different classes of accounts in the months preceding the elections, held between 23rd and 26th of May, 2019. We collected almost 400,000 tweets posted by 863 accounts having different roles in the public society. Through a thorough quantitative analysis we investigate the information flow among them, also exploiting geolocalized information. Accounts show the tendency to confine their interaction within the same class and the debate rarely crosses national borders. Moreover, we do not find evidence of an organized network of accounts aimed at spreading disinformation. Instead, disinformation outlets are largely ignored by the other actors and hence play a peripheral role in online political discussions.

Economic and social consequences of human mobility restrictions under COVID-19.

In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.