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This study examines Facebook and YouTube content from over a thousand news outlets in four European languages from 2018 to 2023, using a Bayesian structural time-series model to evaluate the impact of viral posts. Our results show that most viral events do not significantly increase engagement and rarely lead to sustained growth. The virality effect usually depends on the engagement trend preceding the viral post, typically reversing it. When news emerges unexpectedly, viral events enhances users' engagement, reactivating the collective response process. In contrast, when virality manifests after a sustained growth phase, it represents the final burst of that growth process, followed by a decline in attention. Moreover, quick viral effects fade faster, while slower processes lead to more persistent growth. These findings highlight the transient effect of viral events and underscore the importance of consistent, steady attention-building strategies to establish a solid connection with the user base rather than relying on sudden visibility spikes.

Social media platforms significantly influence ideological divisions by enabling users to select information that aligns with their beliefs and avoid opposing viewpoints. Analyzing approximately 47 million Facebook posts, this study investigates the interactions of around 170 million users with news pages, revealing distinct patterns based on political orientations. While users generally prefer content that reflects their political biases, the extent of engagement varies even among individuals with similar ideological leanings. Specifically, political biases heavily influence commenting behaviors, particularly among users leaning towards the center-left and the right. Conversely, the 'likes' from center-left and centrist users are more indicative of their political affiliations. This research illuminates the complex relationship between social media behavior and political polarization, offering new insights into the manifestation of ideological divisions online.

Understanding the impact of digital platforms on user behavior presents foundational challenges, including issues related to polarization, misinformation dynamics, and variation in news consumption. Comparative analyses across platforms and over different years can provide critical insights into these phenomena. This study investigates the linguistic characteristics of user comments over 34 years, focusing on their complexity and temporal shifts. Utilizing a dataset of approximately 300 million English comments from eight diverse platforms and topics, we examine the vocabulary size and linguistic richness of user communications and their evolution over time. Our findings reveal consistent patterns of complexity across social media platforms and topics, characterized by a nearly universal reduction in text length, diminished lexical richness, but decreased repetitiveness. Despite these trends, users consistently introduce new words into their comments at a nearly constant rate. This analysis underscores that platforms only partially influence the complexity of user comments. Instead, it reflects a broader, universal pattern of human behaviour, suggesting intrinsic linguistic tendencies of users when interacting online.

In 2024, half of the global population is expected to participate in elections, offering researchers a unique opportunity to study online information diffusion and user behavior. This study investigates the media landscape on social media by analyzing Facebook posts from national political parties and major news agencies across Europe, Mexico, and India. Our methodology identifies key topics and evaluates public interaction, reflecting broader trends in political engagement. Using Principal Component Analysis, we distil these topics to uncover patterns of correlation and differentiation. This approach reveals dominant themes that engage global audiences, providing critical insights into the interplay between public opinion and digital narratives during a major electoral cycle. Our findings highlight how different topics resonate across political spectrums, shaping political debate and offering a comprehensive view of the interaction between media content, political ideology, and audience engagement.

Initially conceived for entertainment, social media platforms have profoundly transformed the dissemination of information and consequently reshaped the dynamics of agenda-setting. In this scenario, understanding the factors that capture audience attention and drive viral content is crucial. Employing Gibrat's Law, which posits that an entity's growth rate is unrelated to its size, we examine the engagement growth dynamics of news outlets on social media. Our analysis encloses the Facebook historical data of over a thousand news outlets, encompassing approximately 57 million posts in four European languages from 2008 to the end of 2022. We discover universal growth dynamics according to which news virality is independent of the traditional size or engagement with the outlet. Moreover, our analysis reveals a significant long-term impact of news source reliability on engagement growth, with engagement induced by unreliable sources decreasing over time. We conclude the paper by presenting a statistical model replicating the observed growth dynamics.

In the last years, social media has gained an unprecedented amount of attention, playing a pivotal role in shaping the contemporary landscape of communication and connection. However, Coordinated Inhautentic Behaviour (CIB), defined as orchestrated efforts by entities to deceive or mislead users about their identity and intentions, has emerged as a tactic to exploit the online discourse. In this study, we quantify the efficacy of CIB tactics by defining a general framework for evaluating the influence of a subset of nodes in a directed tree. We design two algorithms that provide optimal and greedy post-hoc placement strategies that lead to maximising the configuration influence. We then consider cascades from information spreading on Twitter to compare the observed behaviour with our algorithms. The results show that, according to our model, coordinated accounts are quite inefficient in terms of their network influence, thus suggesting that they may play a less pivotal role than expected. Moreover, the causes of these poor results may be found in two separate aspects: a bad placement strategy and a scarcity of resources.

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.

In the intricate landscape of social media, genuine content dissemination may be altered by a number of threats. Coordinated Behavior (CB), defined as orchestrated efforts by entities to deceive or mislead users about their identity and intentions, emerges as a tactic to exploit or manipulate online discourse. This study delves into the relationship between CB and toxic conversation on X (formerly known as Twitter). Using a dataset of 11 million tweets from 1 million users preceding the 2019 UK general election, we show that users displaying CB typically disseminate less harmful content, irrespective of political affiliation. However, distinct toxicity patterns emerge among different coordinated cohorts. Compared to their non-CB counterparts, CB participants show marginally higher toxicity levels only when considering their original posts. We further show the effects of CB-driven toxic content on non-CB users, gauging its impact based on political leanings. Our findings suggest that CB only has a limited impact on the toxicity of digital discourse., Comment: Please cite published version: https://doi.org/10.1016/j.osnem.2024.100289

The web radically changed the dissemination of information and the global spread of news. In this study, we aim to reconstruct the connectivity patterns within nations shaping news propagation globally in 2022. We do this by analyzing a dataset of unprecedented size, containing 140 million news articles from 183 countries and related to 37,802 domains in the GDELT database. Unlike previous research, we focus on the sequential mention of events across various countries, thus incorporating a temporal dimension into the analysis of news dissemination networks. Our results show a significant imbalance in online news spreading. We identify news superspreaders forming a tightly interconnected rich club, exerting significant influence on the global news agenda. To further investigate the mechanisms underlying news dissemination and the shaping of global public opinion, we model countries' interactions using a gravity model, incorporating economic, geographical, and cultural factors. Consistent with previous studies, we find that countries' GDP is one of the main drivers to shape the worldwide news agenda.

The public understanding of climate change plays a critical role in translating climate science into climate action. In the public discourse, climate impacts are often discussed in the context of extreme weather events. Here, we analyse 65 million Twitter posts and 240 thousand news media articles related to 18 major hurricanes from 2010 to 2022 to clarify how hurricanes impact the public discussion around climate change. First, we analyse news content and show that climate change is the most prominent non-hurricane specific topic discussed by the news media in relation to hurricanes. Second, we perform a comparative analysis between reliable and questionable news media outlets, finding that the language around climate change varies between news media providers. Finally, using geolocated data, we show that accounts in regions affected by hurricanes discuss climate change at a significantly higher rate than accounts in unaffected areas, with references to climate change increasing by, on average, 80% after impact, and up to 200% for the largest hurricanes. Our findings demonstrate how hurricanes have a key impact on the public awareness of climate change.

Social media platforms are like giant arenas where users can rely on different content and express their opinions through likes, comments, and shares. However, do users welcome different perspectives or only listen to their preferred narratives? This paper examines how users explore the digital space and allocate their attention among communities on two social networks, Voat and Reddit. By analysing a massive dataset of about 215 million comments posted by about 16 million users on Voat and Reddit in 2019 we find that most users tend to explore new communities at a decreasing rate, meaning they have a limited set of preferred groups they visit regularly. Moreover, we provide evidence that preferred communities of users tend to cover similar topics throughout the year. We also find that communities have a high turnover of users, meaning that users come and go frequently showing a high volatility that strongly departs from a null model simulating users' behaviour.

Online social media foster the creation of active communities around shared narratives. Such communities may turn into incubators for conspiracy theories -- some spreading violent messages that could sharpen the debate and potentially harm society. To face these phenomena, most social media platforms implemented moderation policies, ranging from posting warning labels up to deplatforming, i.e., permanently banning users. Assessing the effectiveness of content moderation is crucial for balancing societal safety while preserving the right to free speech. In this paper, we compare the shift in behavior of users affected by the ban of two large communities on Reddit, GreatAwakening and FatPeopleHate, which were dedicated to spreading the QAnon conspiracy and body-shaming individuals, respectively. Following the ban, both communities partially migrated to Voat, an unmoderated Reddit clone. We estimate how many users migrate, finding that users in the conspiracy community are much more likely to leave Reddit altogether and join Voat. Then, we quantify the behavioral shift within Reddit and across Reddit and Voat by matching common users. Few migrating zealots drive the growth of the new GreatAwakening community on Voat, while this effect is absent for FatPeopleHate. Finally, conspiracy users migrating from Reddit tend to recreate their previous social network on Voat. Our findings suggest that banning conspiracy communities hosting violent content should be carefully designed, as these communities may be more resilient to deplatforming.

Social media platforms heavily changed how users consume and digest information and, thus, how the popularity of topics evolves. In this paper, we explore the interplay between the virality of controversial topics and how they may trigger heated discussions and eventually increase users' polarization. We perform a quantitative analysis on Facebook by collecting $\sim57M$ posts from $\sim2M$ pages and groups between 2018 and 2022, focusing on engaging topics involving scandals, tragedies, and social and political issues. Using logistic functions, we quantitatively assess the evolution of these topics finding similar patterns in their engagement dynamics. Finally, we show that initial burstiness may predict the rise of users' future adverse reactions regardless of the discussed topic.

Users online tend to join polarized groups of like-minded peers around shared narratives, forming echo chambers. The echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic models to explore the mechanisms behind the emergence of polarization and echo chambers. The objective was to determine the key factors contributing to these phenomena and identify their interplay. However, the validation of model predictions with empirical data still displays two main drawbacks: lack of systematicity and qualitative analysis. In our work, we bridge this gap by providing a method to numerically compare the opinion distributions obtained from simulations with those measured on social media. To validate this procedure, we develop an opinion dynamic model that takes into account the interplay between human and algorithmic factors. We subject our model to empirical testing with data from diverse social media platforms and benchmark it against two state-of-the-art models. To further enhance our understanding of social media platforms, we provide a synthetic description of their characteristics in terms of the model's parameter space. This representation has the potential to facilitate the refinement of feed algorithms, thus mitigating the detrimental effects of extreme polarization on online discourse., Comment: Accepted for publication in Information Sciences

Climate change and political polarisation are two of the 21st century's critical socio-political issues. Here, we investigate their intersection by studying the discussion around the UN Conference of The Parties on Climate Change (COP) using Twitter data from 2014 to 2021. First, we reveal a large increase in ideological polarisation during COP26, following low polarisation between COP20 and COP25. Second, we show that this increase is driven by growing right-wing activity, a 4-fold increase since COP21 relative to pro-climate groups. Finally, we identify a broad range of ''climate contrarian'' views during COP26, emphasising the theme of ''political hypocrisy'' as a topic of cross-ideological appeal; contrarian views and accusations of hypocrisy have become key themes in the Twitter climate discussion since 2019. With future climate action reliant on negotiations at COP27 and beyond, our results highlight the importance of monitoring polarisation, and its impacts, in the public climate discourse., Comment: 13 pages main, 4 pages extended dta

How information consumption affects behaviour is an open and widely debated research question. A popular hypothesis states that the so-called infodemic has a substantial impact on orienting individual decisions. A competing hypothesis stresses that exposure to vast amounts of even contradictory information has little effect on personal choices. The COVID-19 pandemic offered an opportunity to investigate this relationship, analysing the interplay between COVID-19 related information circulation and the propensity of users to get vaccinated. We analyse the vaccine infodemics on Twitter and Facebook by looking at 146M contents produced by 20M accounts between 1 January 2020 and 30 April 2021. We find that vaccine-related news triggered huge interest through social media, affecting attention patterns and the modality in which information was spreading. However, we observe that such a tumultuous information landscape translated only in minimal variations in overall vaccine acceptance as measured by Facebook's daily COVID-19 Trends and Impact Survey (previously known as COVID-19 World Symptoms Survey) on a sample of 1.6M users. Notably, the observation period includes the European Medicines Agency (EMA) investigations over blood clots cases potentially related to vaccinations, a series of events that could have eroded trust in vaccination campaigns. We conclude the paper by investigating the numerical correlation between various infodemics indices and vaccine acceptance, observing strong compatibility with a null model. This finding supports the hypothesis that altered information consumption patterns are not a reliable predictor of collective behavioural change. Instead, wider attention on social media seems to resolve in polarisation, with the vaccine-prone and the vaccine-hesitant maintaining their positions.

The spread of inaccurate and misleading information may alter behaviours and complicate crisis management, especially during an emergency like the COVID-19 pandemic. This paper aims to investigate information diffusion during the COVID-19 pandemic by evaluating news consumption on YouTube. First, we analyse more than 2 million users' engagement with 13,000 videos released by 68 YouTube channels, labelled with a political bias and fact-checking index. Then, we study the relationship between each user\~Os political preference and their consumption of questionable (i.e., poorly fact-checked) and reliable information. Our results, quantified using measures from information theory, provide evidence for the existence of echo chambers across two dimensions represented by political bias and the trustworthiness of information channels. We observe that the echo chamber structure cannot be reproduced after properly randomising the users' interaction patterns. Moreover, we observe a relation between the political bias of users and their tendency to consume highly questionable news.

Users online tend to consume information adhering to their system of beliefs and to ignore dissenting information. During the COVID-19 pandemic, users get exposed to a massive amount of information about a new topic having a high level of uncertainty. In this paper, we analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation concerning COVID-19. We compare the two platforms on about three million pieces of content analyzing user interaction with respect to news articles. We first describe users' consumption patterns on the two platforms focusing on the political leaning of news outlets. Finally, we characterize the echo chamber effect by modeling the dynamics of users' interaction networks. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content, with a consequent affiliation towards reliable sources in terms of engagement and comments. Conversely, the lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior. Twitter users show segregation towards reliable content with a uniform narrative. Gab, instead, offers a more heterogeneous structure where users, independently of their leaning, follow people who are slightly polarized towards questionable news.

Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "serial haters", intended as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.

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 from Reddit over ten years, from 2011 to 2020, in terms of their statistical complexity and entropy. We find support for the hypothesis that memes are part of an emerging form of internet metalanguage: on one side, we observe an exponential growth with a doubling time of approximately 6 months; on the other side, the complexity of memes contents increases, allowing and adapting to represent social trends and attitudes.

Most of the information operations involve users who may foster polarization and distrust toward science and mainstream journalism, without these users being conscious of their role. Gab is well known to be an extremist-friendly platform that performs little control on the posted content. Thus it represents an ideal benchmark for studying phenomena potentially related to polarization such as misinformation spreading. The combination of these factors may lead to hate as well as to episodes of harm in the real world. In this work we provide a characterization of the interaction patterns within Gab around the COVID-19 topic. To assess the spreading of different content type, we analyze consumption patterns based on both interaction type and source reliability. Overall we find that there are no strong statistical differences in the social response to questionable and reliable content, both following a power law distribution. However, questionable and reliable sources display structural and topical differences in the use of hashtags. The commenting behaviour of users in terms of both lifetime and sentiment reveals that questionable and reliable posts are perceived in the same manner. We can conclude that despite evident differences between questionable and reliable posts Gab users do not perform such a differentiation thus treating them as a whole. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation., Comment: The paper is now replaced with an updated version: arXiv:2106.03924

The policies implemented to hinder the COVID-19 outbreak represent one of the largest critical events in history. The understanding of this process is fundamental for crafting and tailoring post-disaster relief. In this work we perform a massive data analysis, through geolocalized data from 13M Facebook users, on how such a stress affected mobility patterns in France, Italy and UK. We find that the general reduction of the overall efficiency in the network of movements is accompanied by geographical fragmentation with a massive reduction of long-range connections. The impact, however, differs among nations according to their initial mobility structure. Indeed, we find that the mobility network after the lockdown is more concentrated in the case of France and UK and more distributed in Italy. Such a process can be approximated through percolation to quantify the substantial impact of the lockdown.

Recent studies have shown that online users tend to select information adhering to their system of beliefs, ignore information that does not, and join groups - i.e., echo chambers - around a shared narrative. Although a quantitative methodology for their identification is still missing, the phenomenon of echo chambers is widely debated both at scientific and political level. To shed light on this issue, we introduce an operational definition of echo chambers and perform a massive comparative analysis on more than 1B pieces of contents produced by 1M users on four social media platforms: Facebook, Twitter, Reddit, and Gab. We infer the leaning of users about controversial topics - ranging from vaccines to abortion - and reconstruct their interaction networks by analyzing different features, such as shared links domain, followed pages, follower relationship and commented posts. Our method quantifies the existence of echo-chambers along two main dimensions: homophily in the interaction networks and bias in the information diffusion toward likely-minded peers. We find peculiar differences across social media. Indeed, while Facebook and Twitter present clear-cut echo chambers in all the observed dataset, Reddit and Gab do not. Finally, we test the role of the social media platform on news consumption by comparing Reddit and Facebook. Again, we find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.

In response to the COVID-19 pandemic, National governments have applied lockdown restrictions to reduce the infection rate. We perform a massive analysis on near real-time Italian 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 a segregation effect, since mobility restrictions are stronger in municipalities for which inequality is higher and where individuals have lower income per capita.

We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that an early lockdown shifts the epidemic in time, while that beyond a critical value of the lockdown strength, the epidemic tend to restart after lifting the restrictions. As a consequence, specific mitigation strategies must be introduced. We characterize the relative importance of different broad strategies by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian regions as separate administrative entities, in which social interactions between age classs occur. Due to the sparsity of the inter-regional mobility matrix, once started the epidemics tend to develop independently across areas, justifying the adoption of solutions specific to individual regions or to clusters of regions. Second, we show that social contacts between age classes play a fundamental role and that measures which take into account the age structure of the population can provide a significant contribution to mitigate the rebound effects. Our model is general, and while it does not analyze specific mitigation strategies, it highlights the relevance of some key parameters on non-pharmaceutical mitigation mechanisms for the epidemics.

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 numbers $R_0$ for each social media platform. Moreover, we characterize 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 massive diffusion of social media fosters disintermediation and changes the way users are informed, the way they process reality, and the way they engage in public debate. The cognitive layer of users and the related social dynamics define the nature and the dimension of informational threats. Users show the tendency to interact with information adhering to their preferred narrative and to ignore dissenting information. Confirmation bias seems to account for users decisions about consuming and spreading content; and, at the same time, aggregation of favored information within those communities reinforces group polarization. In this work, the authors address the problem of (mis)information operations with a holistic and integrated approach. Cognitive weakness induced by this new information environment are considered. Moreover, (mis)information operations, with particular reference to the Italian context, are considered; and the fact that the phenomenon is more complex than expected is highlighted. The paper concludes by providing an integrated research roadmap accounting for the possible future technological developments., Comment: The paper first appeared in Volume 18, Issue 3 of the Journal of Information Warfare

Selective exposure is the main driver for the economy of attention when consuming online content. We select information adhering to our system of beliefs and ignore dissenting information. However, even personal interest is likely to play a role in determining our attention patterns. To understand in more detail the dynamics of interest-driven choices, we address the evolution of users' interest on Reddit by means of an analysis on more than 7 million of users. We observe that the lifetime distribution of users on subreddits (online thematic communities) is 'short' with respect to the observation period. Furthermore, users tend to be active on a very limited number of subreddits with respect to the wide offer of the platform. These aspects indicate the presence of a migrating behavior of users among subreddits. To characterize this phenomenon we propose a metric based on a geometrical encoding of the 'interest space' of the user. The movement of users across subreddits is characterized by a bursty trend, made of sudden variations. The most frequent of them take place between recreational subreddits and those more related to news and politics. We describe this kind of activity with two behaviors called interest drift and interest shift. Our results suggest that selective exposure and personal interest coexist in driving content consumption.

The advent of social media changed the way we consume content favoring a disintermediated access and production. This scenario has been matter of critical discussion about its impact on society. Magnified in the case of Arab Spring or heavily criticized in the Brexit and 2016 U.S. elections. In this work we explore information consumption on Twitter during the last European electoral campaign by analyzing the interaction patterns of official news sources, fake news sources, politicians, people from the showbiz and many others. We extensively explore interactions among different classes of accounts in the months preceding the last European 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 any 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.

Despite their playful purpose social media changed the way users access information, debate, and form their opinions. Recent studies, indeed, showed that users online tend to promote their favored narratives and thus to form polarized groups around a common system of beliefs. Confirmation bias helps to account for users decisions about whether to spread content, thus creating informational cascades within identifiable communities. At the same time, aggregation of favored information within those communities reinforces selective exposure and group polarization. Along this path, through a thorough quantitative analysis we approach connectivity patterns over 1.2M of Facebook users engaged with two very conflicting narratives: scientific and conspiracy news. Analyzing such data, we quantitatively investigate the effect of two mechanisms (namely challenge avoidance and reinforcement seeking) behind confirmation bias, one of the major drivers of human behavior in social media. We find that challenge avoidance mechanism triggers the emergence of two distinct and polarized groups of users (i.e., echo chambers) who also tend to be surrounded by friends having similar systems of beliefs. Through a network based approach, we show how the reinforcement seeking mechanism limits the influence of neighbors and primarily drives the selection and diffusion of contents even among like-minded users, thus fostering the formation of highly polarized sub-clusters within the same echo chamber. Finally, we show that polarized users reinforce their preexisting beliefs by leveraging the activity of their like-minded neighbors, and this trend grows with the user engagement suggesting how peer influence acts as a support for reinforcement seeking., Comment: This version removed by arXiv administrators due to concern of copyright infringement

Recent studies, targeting Facebook, showed the tendency of users to interact with information adhering to their preferred narrative and to ignore dissenting information. Primarily driven by confirmation bias, users tend to join polarized clusters where they cooperate to reinforce a like-minded system of beliefs, thus facilitating fake news and misinformation cascades. To gain a deeper understanding of these phenomena, in this work we analyze the lexicons used by the communities of users emerging on Facebook around verified and unverified contents. We show how the lexical approach provides important insights about the kind of information processed by the two communities of users and about their overall sentiment. Furthermore, by focusing on comment threads, we observe a strong positive correlation between the lexical convergence of co-commenters and their number of interactions, which in turns suggests that such a trend could be a proxy for the emergence of collective identities and polarization in opinion dynamics., Comment: 11 pages, 9 figures

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, 2020

Within OSNs, many of our supposedly online friends may instead be fake accounts called social bots, part of large groups that purposely re-share targeted content. Here, we study retweeting behaviors on Twitter, with the ultimate goal of detecting retweeting social bots. We collect a dataset of 10M retweets. We design a novel visualization that we leverage to highlight benign and malicious patterns of retweeting activity. In this way, we uncover a 'normal' retweeting pattern that is peculiar of human-operated accounts, and 3 suspicious patterns related to bot activities. Then, we propose a bot detection technique that stems from the previous exploration of retweeting behaviors. Our technique, called Retweet-Buster (RTbust), leverages unsupervised feature extraction and clustering. An LSTM autoencoder converts the retweet time series into compact and informative latent feature vectors, which are then clustered with a hierarchical density-based algorithm. Accounts belonging to large clusters characterized by malicious retweeting patterns are labeled as bots. RTbust obtains excellent detection results, with F1 = 0.87, whereas competitors achieve F1 < 0.76. Finally, we apply RTbust to a large dataset of retweets, uncovering 2 previously unknown active botnets with hundreds of accounts.

Users online tend to acquire information adhering to their system of beliefs and to ignore dissenting information. Such dynamics might affect page popularity. In this paper we introduce an algorithm, that we call PopRank, to assess both the Impact of Facebook pages as well as users' Engagement on the basis of their mutual interactions. The ideas behind the PopRank are that i) high impact pages attract many users with a low engagement, which means that they receive comments from users that rarely comment, and ii) high engagement users interact with high impact pages, that is they mostly comment pages with a high popularity. The resulting ranking of pages can predict the number of comments a page will receive and the number of its posts. Pages impact turns out to be slightly dependent on pages' informative content (e.g., science vs conspiracy) but independent of users' polarization., Comment: 10 pages, 5 figures

The advent of WWW changed the way we can produce and access information. Recent studies showed that users tend to select information that is consistent with their system of beliefs, forming polarized groups of like-minded people around shared narratives where dissenting information is ignored. In this environment, users cooperate to frame and reinforce their shared narrative making any attempt at debunking inefficient. Such a configuration occurs even in the consumption of news online, and considering that 63% of users access news directly form social media, one hypothesis is that more polarization allows for further spreading of misinformation. Along this path, we focus on the polarization of users around news outlets on Facebook in different European countries (Italy, France, Spain and Germany). First, we compare the pages' posting behavior and the users' interacting patterns across countries and observe different posting, liking and commenting rates. Second, we explore the tendency of users to interact with different pages (i.e., selective exposure) and the emergence of polarized communities generated around specific pages. Then, we introduce a new metric -- i.e., polarization rank -- to measure polarization of communities for each country. We find that Italy is the most polarized country, followed by France, Germany and lastly Spain. Finally, we present a variation of the Bounded Confidence Model to simulate the emergence of these communities by considering the users' engagement and trust on the news. Our findings suggest that trust in information broadcaster plays a pivotal role against polarization of users online.

Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, "predicting" future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.

Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential powerful influence factor to hesitancy. Recent studies in other fields than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select the information adhering to theirs system of beliefs and tend to ignore dissenting information. In this paper we assess whether there is polarization in Social Media use in the field of vaccination. We perform a thorough quantitative analysis on Facebook analyzing 2.6M users interacting with 298.018 posts over a time span of seven years and 5 months. We used community detection algorithms to automatically detect the emergent communities from the users activity and to quantify the cohesiveness over time of the communities. Our findings show that content consumption about vaccines is dominated by the echo-chamber effect and that polarization increased over years. Communities emerge from the users consumption habits, i.e. the majority of users only consumes information in favor or against vaccines, not both. The existence of echo-chambers may explain why social-media campaigns providing accurate information may have limited reach, may be effective only in sub-groups and might even foment further polarization of opinions. The introduction of dissenting information into a sub-group is disregarded and can have a backfire effect, further reinforcing the existing opinions within the sub-group.

Social media are pervaded by unsubstantiated or untruthful rumors, that contribute to the alarming phenomenon of misinformation. The widespread presence of a heterogeneous mass of information sources may affect the mechanisms behind the formation of public opinion. Such a scenario is a florid environment for digital wildfires when combined with functional illiteracy, information overload, and confirmation bias. In this essay, we focus on a collection of works aiming at providing quantitative evidence about the cognitive determinants behind misinformation and rumor spreading. We account for users' behavior with respect to two distinct narratives: a) conspiracy and b) scientific information sources. In particular, we analyze Facebook data on a time span of five years in both the Italian and the US context, and measure users' response to i) information consistent with one's narrative, ii) troll contents, and iii) dissenting information e.g., debunking attempts. Our findings suggest that users tend to a) join polarized communities sharing a common narrative (echo chambers), b) acquire information confirming their beliefs (confirmation bias) even if containing false claims, and c) ignore dissenting information.