Emotional Dynamics in the Age of Misinformation

The media risk of infodemic in public health emergencies: Consequences and mitigation approaches

This study explores the nuances of information sharing in the context of infodemics, with a concentrated examination of the effects of opinion leaders and information attention on users' disposition towards sharing information during public health emergencies. The research adopts a quantitative methodology, employing Structural Equation Modeling (SEM) to empirically test the proposed hypotheses. By employing a rigorous analytical framework, the research also scrutinizes the mediating role of risk perception in shaping users' intentions to disseminate information related to public health emergencies. Additionally, it investigates the moderating effect of perceived usefulness, shedding light on how it influences the strength of the relationship between information attention and risk perception. The findings underscore the significance for public health communication strategies, emphasizing targeted messaging utilizing trusted opinion leaders and emphasizing information utility to foster responsible sharing. This research contributes to the academic conversation on infodemic management, providing empirical insights to guide policies and practices in mitigating misinformation during public health emergencies.

Entropy-based detection of Twitter echo chambers

Echo chambers, i.e. clusters of users exposed to news and opinions in line with their previous beliefs, were observed in many online debates on social platforms. We propose a completely unbiased entropy-based method for detecting echo chambers. The method is completely agnostic to the nature of the data. In the Italian Twitter debate about the Covid-19 vaccination, we find a limited presence of users in echo chambers (about 0.35% of all users). Nevertheless, their impact on the formation of a common discourse is strong, as users in echo chambers are responsible for nearly a third of the retweets in the original dataset. Moreover, in the case study observed, echo chambers appear to be a receptacle for disinformative content.

Exposing influence campaigns in the age of LLMs: a behavioral-based AI approach to detecting state-sponsored trolls

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the "Troll Score", quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its performance in the context of the 2016 Russian interference campaign during the U.S. Presidential election. Our experiments yield compelling results, demonstrating that our approach can identify account sequences with an AUC close to 99% and accurately differentiate between Russian trolls and organic users with an AUC of 91%. Notably, our behavioral-based approach holds a significant advantage in the ever-evolving landscape, where textual and linguistic properties can be easily mimicked by Large Language Models (LLMs): In contrast to existing language-based techniques, it relies on more challenging-to-replicate behavioral cues, ensuring greater resilience in identifying influence campaigns, especially given the potential increase in the usage of LLMs for generating inauthentic content. Finally, we assessed the generalizability of our solution to various entities driving different information operations and found promising results that will guide future research.

Online Interaction Turns the Congeniality Bias Into an Uncongeniality Bias

Online phenomena like echo chambers and polarization are believed to be driven by humans' penchant to selectively expose themselves to attitudinally congenial content. However, if like-minded content were the only predictor of online behavior, heated debate and flaming on the Internet would hardly occur. Research has overlooked how online behavior changes when people are given an opportunity to reply to dissenters. Three experiments (total N = 320; convenience student samples from Germany) and an internal meta-analysis show that in a discussion-forum setting where participants can reply to earlier comments larger cognitive conflict between participant attitude and comment attitude predicts higher likelihood to respond (uncongeniality bias). When the discussion climate was friendly (vs. oppositional) to the views of participants, the uncongeniality bias was more pronounced and was also associated with attitude polarization. These results suggest that belief polarization on social media may not only be driven by congeniality but also by conflict.

The role of perceived minority-group status in the conspiracy beliefs of factual majority groups

Research suggests that minority-group members sometimes are more susceptible to misinformation. Two complementary studies examined the influence of perceived minority status on susceptibility to misinformation and conspiracy beliefs. In study 1 (n = 2140), the perception of belonging to a minority group, rather than factually belonging to it, was most consistently related with an increased susceptibility to COVID-19 misinformation across national samples from the USA, the UK, Germany and Poland. Specifically, perceiving that one belongs to a gender minority group particularly predicted susceptibility to misinformation when participants factually did not belong to it. In pre-registered study 2 (n = 1823), an experiment aiming to manipulate the minority perceptions of men failed to influence conspiracy beliefs in the predicted direction. However, pre-registered correlational analyses showed that men who view themselves as a gender minority were more prone to gender conspiracy beliefs and exhibited a heightened conspiracy mentality. This effect was correlationally mediated by increased feelings of system identity threat, collective narcissism, group relative deprivation and actively open-minded thinking. Especially, the perception of being a minority in terms of power and influence (as compared to numerically) was linked to these outcomes. We discuss limitations and practical implications for countering misinformation.

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

Negativity drives online news consumption

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here we analyse the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ~105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more prevalent than negative words, we found that negative words in news headlines increased consumption rates (and positive words decreased consumption rates). For a headline of average length, each additional negative word increased the click-through rate by 2.3%. Our results contribute to a better understanding of why users engage with online media.

What Are Conspiracy Theories? A Definitional Approach to Their Correlates, Consequences, and Communication

Conspiracy theories are abundant in social and political discourse, with serious consequences for individuals, groups, and societies. However, psychological scientists have started paying close attention to them only in the past 20 years. We review the spectacular progress that has since been made and some of the limitations of research so far, and we consider the prospects for further progress. To this end, we take a step back to analyze the defining features that make conspiracy theories different in kind from other beliefs and different in degree from each other. We consider how these features determine the adoption, consequences, and transmission of belief in conspiracy theories, even though their role as causal or moderating variables has seldom been examined. We therefore advocate for a research agenda in the study of conspiracy theories that starts-as is routine in fields such as virology and toxicology-with a robust descriptive analysis of the ontology of the entity at its center.

[Conspiracy as a surrogate for religion? Religiosity, spirituality, and affinity towards conspiracy myths in times of crisis]

In times of crisis, alternative interpretations of the world that challenge the public sphere gain impact. During the COVID-19 pandemic, such narratives spread in the form of conspiracy myths. They give explanations for complex questions by assuming that social events are planned and controlled by secret elites. Moreover, they are often based on pseudo-transcendental explanations that defy scrutiny but fill fateful events with meaning. In this context, the question arises whether conspiracy myths contain religious or spiritual elements. In this study, we use a representative population survey conducted in April 2022 in German- and French-speaking Switzerland (n = 1221) to examine the relationship between spirituality, religiosity, and affinity toward conspiracy myths. The results show that emotional concern during the COVID-19 pandemic is an important predictor related to conspiracy affinity. The more negative the emotional concern, the higher the affinity towards conspiracy myths. Conversely, positive emotions during the pandemic negatively affect conspiracy affinity. While religious affiliations and religiosity hardly influence conspiracy affinity, spirituality turns out to be the most important predictor in our study. We conclude that in secularized societies spirituality without involvement in traditional religious communities can result in an increased openness to conspiracy myths as a surrogate for religion.

Emotional profiling and cognitive networks unravel how mainstream and alternative press framed AstraZeneca, Pfizer and COVID-19 vaccination campaigns

COVID-19 vaccines have been largely debated by the press. To understand how mainstream and alternative media debated vaccines, we introduce a paradigm reconstructing time-evolving narrative frames via cognitive networks and natural language processing. We study Italian news articles massively re-shared on Facebook/Twitter (up to 5 million times), covering 5745 vaccine-related news from 17 news outlets over 8 months. We find consistently high trust/anticipation and low disgust in the way mainstream sources framed "vaccine/vaccino". These emotions were crucially missing in alternative outlets. News titles from alternative sources framed "AstraZeneca" with sadness, absent in mainstream titles. Initially, mainstream news linked mostly "Pfizer" with side effects (e.g. "allergy", "reaction", "fever"). With the temporary suspension of "AstraZeneca", negative associations shifted: Mainstream titles prominently linked "AstraZeneca" with side effects, while "Pfizer" underwent a positive valence shift, linked to its higher efficacy. Simultaneously, thrombosis and fearful conceptual associations entered the frame of vaccines, while death changed context, i.e. rather than hopefully preventing deaths, vaccines could be reported as potential causes of death, increasing fear. Our findings expose crucial aspects of the emotional narratives around COVID-19 vaccines adopted by the press, highlighting the need to understand how alternative and mainstream media report vaccination news.

The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study

BACKGROUND

Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral.

OBJECTIVE

This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media.

METHODS

We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate.

RESULTS

Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications.

CONCLUSIONS

Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.

Meta-Reflexivity as a Way toward Responsible and Sustainable Behavior

In line with the social morphogenetic approach, this article explores the role of meta-reflexivity in responsible concerns and actions oriented toward achieving a sustainable society. Based on the case study of Slovenia, this article addresses individuals’ social and environmental responsibility by considering the relationships between their attitudes, intentions and behavior. It draws on a survey questionnaire that includes the reflexivity measurement tool. The path-analysis is applied to consider the aspects of responsibility as endogenous variables, while the social/cultural conditions (age, gender, educational level, income and the survey wave) and meta-reflexivity as a specific mode of inner dialog are included as exogenous variables. A coherent index of socially and environmentally responsible behavior can be constructed and explained by social/cultural conditions and meta-reflexivity. The COVID-19 pandemic indicates negative effects on responsibility, mostly due to a decline in meta-reflexivity. The study reveals two different—although not mutually exclusive—paths towards socially and environmentally responsible behavior. The first one is based on a combination of well-established values, habits and inertia. This behavior is more typical for older generations, as indicated by the impact of age. The second one is mostly based on critical, meta-reflexive thinking and it is more typical for younger, more educated and more affluent people.

Netiquette for social media engagement for oncology professionals

Social media growth has revolutionized health care, facilitating user-friendly, rapid and global sharing of content. Within oncology, this allows for new frontiers in communication for cancer patients, caregivers and healthcare providers. As more physicians engage in online spaces, it is imperative that there are resources to assist in establishing a professional presence on social media. This article describes how to create a social media identity, best practices for engaging both in patient and caregiver spaces and professional communities, and how to address antagonistic and inappropriate behavior on social media with the goal of helping physicians develop an engaging, productive and enjoyable experience online.

Users’ polarisation in dynamic discussion networks: The case of refugee crisis in Sweden

This paper presents a study on the dynamics of sentiment polarisation in the active online discussion communities formed around a controversial topic—immigration. Using a collection of tweets in the Swedish language from 2012 to 2019, we track the development of the communities and their sentiment polarisation trajectories over time and in the context of an exogenous shock represented by the European refugee crisis in 2015. To achieve the goal of the study, we apply methods of network and sentiment analysis to map users’ interactions in the network communities and quantify users’ sentiment polarities. The results of the analysis give little evidence for users’ polarisation in the network and its communities, as well as suggest that the crisis had a limited effect on the polarisation dynamics on this social media platform. Yet, we notice a shift towards more negative tonality of users’ sentiments after the crisis and discuss possible explanations for the above-mentioned observations.

The importance of social media users' responses in tackling digital COVID-19 misinformation in Africa

Objective

Digital technologies present both an opportunity and a threat for advancing public health. At a time of pandemic, social media has become a tool for the rapid spread of misinformation. Mitigating the impacts of misinformation is particularly acute across Africa, where WhatsApp and other forms of social media dominate, and where the dual threats of misinformation and COVID-19 threaten lives and livelihoods. Given the scale of the problem within Africa, we set out to understand (i) the potential harm that misinformation causes, (ii) the available evidence on how to mitigate that misinformation and (iii) how user responses to misinformation shape the potential for those mitigating strategies to reduce the risk of harm.

Methods

We undertook a multi-method study, combining a rapid review of the research evidence with a survey of WhatsApp users across Africa.

Results

We identified 87 studies for inclusion in our review and had 286 survey respondents from 17 African countries. Our findings show the considerable harms caused by public health misinformation in Africa and the lack of evidence for or against strategies to mitigate against such harms. Furthermore, they highlight how social media users' responses to public health misinformation can mitigate and exacerbate potential harms. Understanding the ways in which social media users respond to misinformation sheds light on potential mitigation strategies.

Conclusions

Public health practitioners who utilise digital health approaches must not underestimate the importance of considering the role of social media in the circulation of misinformation, nor of the responses of social media users in shaping attempts to mitigate against the harms of such misinformation.

Evolution of topics and hate speech in retweet network communities

Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018-2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post.

Dynamics of online hate and misinformation

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, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "pure haters", meant 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 the 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.

Evidence of disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy

Background

In Italy, in recent years, vaccination coverage for key immunizations as MMR has been declining to worryingly low levels, with large measles outbreaks. As a response in 2017, the Italian government expanded the number of mandatory immunizations introducing penalties to unvaccinated children’s families. During the 2018 general elections campaign, immunization policy entered the political debate with the government in-charge blaming oppositions for fuelling vaccine scepticism. A new government (formerly in the opposition) established in 2018 temporarily relaxed penalties and announced the introduction of forms of flexibility.

Objectives and methods

First, we supplied a definition of disorientation, as the “lack of well-established and resilient opinions among individuals, therefore causing them to change their positions as a consequence of sufficient external perturbations”. Second, procedures for testing for the presence of both short and longer-term collective disorientation in Twitter signals were proposed. Third, a sentiment analysis on tweets posted in Italian during 2018 on immunization topics, and related polarity evaluations, were used to investigate whether the contrasting announcements at the highest political level might have originated disorientation amongst the Italian public.

Results

Vaccine-relevant tweeters’ interactions peaked in response to main political events. Out of retained tweets, 70.0% resulted favourable to vaccination, 16.4% unfavourable, and 13.6% undecided, respectively. The smoothed time series of polarity proportions exhibit frequent large changes in the favourable proportion, superimposed to a clear up-and-down trend synchronized with the switch between governments in Spring 2018, suggesting evidence of disorientation among the public.

Conclusions

The reported evidence of disorientation for opinions expressed in online social media shows that critical health topics, such as vaccination, should never be used to achieve political consensus. This is worsened by the lack of a strong Italian institutional presence on Twitter, calling for efforts to contrast misinformation and the ensuing spread of hesitancy. It remains to be seen how this disorientation will impact future parents’ vaccination decisions.

Microblog credibility indicators regarding misinformation of genetically modified food on Weibo

The considerable amount of misinformation on social media regarding genetically modified (GM) food will not only hinder public understanding but also mislead the public to make unreasoned decisions. This study discovered a new mechanism of misinformation diffusion in the case of GM food and applied a framework of supervised machine learning to identify effective credibility indicators for the misinformation prediction of GM food. Main indicators are proposed, including user identities involved in spreading information, linguistic styles, and propagation dynamics. Results show that linguistic styles, including sentiment and topics, have the dominant predictive power. In addition, among the user identities, engagement, and extroversion are effective predictors, while reputation has almost no predictive power in this study. Finally, we provide strategies that readers should be aware of when assessing the credibility of online posts and suggest improvements that Weibo can use to avoid rumormongering and enhance the science communication of GM food.

A Compression-Based Method for Detecting Anomalies in Textual Data

Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. On this basis, we propose a parameter-free method to detect security incidents from structured text regardless its nature. We use the Normalized Compression Distance to obtain a set of features that can be used by a Support Vector Machine to classify events from a heterogeneous cybersecurity environment. In particular, we explore and validate the application of our method in four different cybersecurity domains: HTTP anomaly identification, spam detection, Domain Generation Algorithms tracking and sentiment analysis. The results obtained show the validity and flexibility of our approach in different security scenarios with a low configuration burden.

The effects of experimentally induced emotions on revising common vaccine misconceptions

Knowledge revision is the process of updating incorrect prior knowledge in light of new, correct information. Although theoretical and empirical knowledge has advanced regarding the cognitive processes involved in revision, less is known about the role of emotions, which have shown inconsistent relations with key revision processes. This study examined the effects of experimentally induced emotions on online and offline knowledge revision of vaccination misconceptions. Before reading refutation and non-refutation texts, 96 individuals received a positive, negative, or no emotion induction. Findings showed that negative emotions, more than positive emotions, resulted in enhanced knowledge revision as indicated by greater ease of integrating correct information during reading and higher comprehension test scores after reading. Findings are discussed with respect to contemporary frameworks of knowledge revision and emotion in reading comprehension and implications for educational practice.

A model for the Twitter sentiment curve

Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Pólya urn, introduced and studied in Aletti and Crimaldi (2019, 2020), which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Pólya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.

Technical assistance in the field of risk communication

This report assesses peer-reviewed and grey literature on risk communication concepts and practices, as requested by the European Commission to support the implementation of a 'General Plan for Risk Communication', i.e. an integrated framework for EU food safety risk assessors and risk managers at Union and national level, as required by the revised EU General Food Law Regulation. We conducted a scoping review of social research studies and official reports in relation to risk communication in the following areas: understanding and awareness of risk analysis roles and tasks, reducing misunderstanding of the different meaning of the terms 'hazard' and 'risk', tackling misinformation and disinformation, enhancing confidence in EU food safety, taking account of risk perceptions, key factors in trade-offs about risks, audience segmentation and tools, channels and mechanisms for coordinated risk communications. We structured our findings as follows: i) definitions of key concepts, ii) audience analysis and information requirements, iii) risk profiling, models and mechanisms, iv) contributions to communication strategies. We make several recommendations for consideration by the Commission, both in terms of actions to support the design and implementation of the general plan, and research needs that we consider crucial to further inform appropriate risk communication in the EU. EFSA carried out a targeted consultation of experts and a public consultation open to all interested parties including the general public, in preparing and finalising this report.

Flow of online misinformation during the peak of the COVID-19 pandemic in Italy

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00289-4.

Three contextual dimensions of information on social media: lessons learned from the COVID-19 infodemic

The COVID-19 pandemic has been accompanied on social media by an explosion of information disorders such as inaccurate, misleading and irrelevant information. Countermeasures adopted thus far to curb these informational disorders have had limited success because these did not account for the diversity of informational contexts on social media, focusing instead almost exclusively on curating the factual content of user's posts. However, content-focused measures do not address the primary causes of the infodemic itself, namely the user's need to post content as a way of making sense of the situation and for gathering reactions of consensus from friends. This paper describes three types of informational context-weak epistemic, strong normative and strong emotional-which have not yet been taken into account by current measures to curb down the informational disorders. I show how these contexts are related to the infodemic and I propose measures for dealing with them for future global crisis situations.

The pharmacist's active role in combating COVID-19 medication misinformation

Pharmacists listen to and dispel medication misinformation daily. Because of their accessibility, pharmacists have the opportunity during pharmacist-patient interactions to begin a dialogue with their patients and provide critical education to improve patient safety and public health. Current examples of pharmacists intervening with medication misinformation include addressing the antivaccination community, educating on the safety of generic drugs, and using evidence-based medicine for antimicrobial stewardship. However, combating medication misinformation in pharmacy practice with patients takes on many forms and can pose a number of challenges. Most recently during the coronavirus disease 2019 (COVID-19) pandemic, a concurrent infodemic has led to claims of pharmacotherapeutic superiority and efficacy unsubstantiated by scientific evidence. Misinformation and partisan politics have also created a distrust in COVID-19 vaccine development. In addition, rogue Internet pharmacies and companies have marketed new and unverified COVID-19 treatments and tests. Pharmacists must actively combat these instances of medication misinformation and educate their patients on how not to fall victim to convincing marketing and misinformation schemes. Pharmacists can help patients recognize misinformation by vetting sources of information and communicating how negative emotional information circulates. In addition, pharmacists combat misinformation with patients by providing accurate alternative explanations in patient-friendly language. Although it is easier to stay silent and let misinformation circulate, pharmacists must work with their health care team members to actively reject misinformation pertaining to medications, COVID-19 pharmacotherapy and vaccinations, and in any future public health crisis.

Reliance on emotion promotes belief in fake news

What is the role of emotion in susceptibility to believing fake news? Prior work on the psychology of misinformation has focused primarily on the extent to which reason and deliberation hinder versus help the formation of accurate beliefs. Several studies have suggested that people who engage in more reasoning are less likely to fall for fake news. However, the role of reliance on emotion in belief in fake news remains unclear. To shed light on this issue, we explored the relationship between experiencing specific emotions and believing fake news (Study 1; N = 409). We found that across a wide range of specific emotions, heightened emotionality at the outset of the study was predictive of greater belief in fake (but not real) news posts. Then, in Study 2, we measured and manipulated reliance on emotion versus reason across four experiments (total N = 3884). We found both correlational and causal evidence that reliance on emotion increases belief in fake news: self-reported use of emotion was positively associated with belief in fake (but not real) news, and inducing reliance on emotion resulted in greater belief in fake (but not real) news stories compared to a control or to inducing reliance on reason. These results shed light on the unique role that emotional processing may play in susceptibility to fake news.

The effects of positive and negative emotional text content on knowledge revision

Across three experiments, we sought to determine the effects of positive and negative emotional content in refutation texts on misconceptions about vaccines. The addition of negative emotional content to texts that identify, refute, and explain vaccine misconceptions improved knowledge revision observed during reading (Experiment 1). However, the addition of positive emotional content to refutation texts weakened this effect (Experiment 2). A direct comparison between negative and positive emotional content provided corroborating evidence for these findings (Experiment 3). Across experiments, results show that all refutation texts (with or without positive or negative emotional content) improved learning assessed after reading. These findings show the differential effects of emotional content on processing misconceptions about an important socio-scientific topic and provide consistent support for refutation texts as a potentially useful tool in these corrective efforts.

Robust Fake News Detection Over Time and Attack

In this study, we examine the impact of time on state-of-the-art news veracity classifiers. We show that, as time progresses, classification performance for both unreliable and hyper-partisan news classification slowly degrade. While this degradation does happen, it happens slower than expected, illustrating that hand-crafted, content-based features, such as style of writing, are fairly robust to changes in the news cycle. We show that this small degradation can be mitigated using online learning. Last, we examine the impact of adversarial content manipulation by malicious news producers. Specifically, we test three types of attack based on changes in the input space and data availability. We show that static models are susceptible to content manipulation attacks, but online models can recover from such attacks.

Role of voting intention in public opinion polarization

We introduce and study a simple model for the dynamics of voting intention in a population of agents that have to choose between two candidates. The level of indecision of a given agent is modeled by its propensity to vote for one of the two alternatives, represented by a variable p∈[0,1]. When an agent i interacts with another agent j with propensity p_{j}, then i either increases its propensity p_{i} by h with probability P_{ij}=ωp_{i}+(1-ω)p_{j}, or decreases p_{i} by h with probability 1-P_{ij}, where h is a fixed step. We assume that the interactions form a complete graph, where each agent can interact with any other agent. We analyze the system by a rate equation approach and contrast the results with Monte Carlo simulations. We find that the dynamics of propensities depends on the weight ω that an agent assigns to its own propensity. When all the weight is assigned to the interacting partner (ω=0), agents' propensities are quickly driven to one of the extreme values p=0 or p=1, until an extremist absorbing consensus is achieved. However, for ω>0 the system first reaches a quasistationary state of symmetric polarization where the distribution of propensities has the shape of an inverted Gaussian with a minimum at the center p=1/2 and two maxima at the extreme values p=0,1, until the symmetry is broken and the system is driven to an extremist consensus. A linear stability analysis shows that the lifetime of the polarized state, estimated by the mean consensus time τ, diverges as τ∼(1-ω)^{-2}lnN when ω approaches 1, where N is the system size. Finally, a continuous approximation allows us to derive a transport equation whose convection term is compatible with a drift of particles from the center toward the extremes.

Partisan public health: how does political ideology influence support for COVID-19 related misinformation?

This study analyzes over 4000 tweets related to six misinformation topics about the COVID-19 pandemic: the use of hydroxychloroquine as treatment, the use of bleach as a preventative measure, Bill Gates intentionally causing the virus, the Chinese Communist Party intentionally causing the virus, and the Deep State causing the virus to ruin the economy and threaten President Trump's reelection chances. Across 5 of 6 topics (excluding bleach), conservatives dominate the discourse on Twitter. Conservatives are also more likely than their liberal peers to believe in and push conspiracy theories that the Chinese Communist Party, Bill Gates, and the Deep State are working in conjunction to infect the population and enact a surveillance state. Pandemic related misinformation has previously been associated with decreased adherence to public health recommendations and adverse health effects and evidence from the current pandemic indicates that adherence to public health recommendations is starkly partisan. This study suggests that the political and informational polarization further facilitated by social media platforms such as Twitter may have dire consequences for public health.

The Web of False Information

A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false-information campaigns with targeted manipulation of public opinion on specific topics. These false-information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one and needs increased public awareness as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community.

In this article, we make a step in this direction by providing a typology of the Web’s false-information ecosystem, composed of various types of false-information, actors, and their motives. We report a comprehensive overview of existing research on the false-information ecosystem by identifying several lines of work: (1) how the public perceives false information; (2) understanding the propagation of false information; (3) detecting and containing false information on the Web; and (4) false information on the political stage. In this work, we pay particular attention to political false information as: (1) it can have dire consequences to the community (e.g., when election results are mutated) and (2) previous work shows that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false-information dissemination on the Web.

Dealing with digital misinformation: a polarised context of narratives and tribes

The advent of the internet and social networks has revolutionised the information space and changed the way in which we communicate and get informed. On the internet, a huge amount of information competes for our (limited) attention. Moreover, despite the increasing quantity of contents, quality may be poor, making the environment particularly florid for misinformation spreading. In such a context, our cognitive biases emerge, first and foremost, confirmation bias, i.e. the human tendency to look for information that is already in agreement with one's system of beliefs. To shade light on the phenomenon, we present a collection of works investigating how information gets consumed and shapes communities on Facebook. We find that confirmation bias plays a crucial role in content selection and diffusion, and we provide empirical evidence of the existence of echo chambers, i.e. well separated and polarised groups of like‐minded users sharing a same narrative. Immersed in these bubbles, users keep framing and reinforcing their world view, ignoring information dissenting from their preferred narrative. In this scenario, corrections in the form of fact‐checking or debunking attempts seem to fail and have instead a backfire effect. To contrast misinformation, smoothing polarisation is so essential, and may require the design of tailored counter‐narratives and appropriate communication strategies, particularly for sensitive topics.

Network model of conviction-driven social segregation

To measure, predict, and prevent social segregation, it is necessary to understand the factors that cause it. While in most available descriptions space plays an essential role, one outstanding question is whether and how this phenomenon is possible in a well-mixed social network. We define and solve a simple model of segregation on networks based on discrete convictions. In our model, space does not play a role, and individuals never change their conviction, but they may choose to connect socially to other individuals based on two criteria: sharing the same conviction and individual popularity (regardless of conviction). The tradeoff between these two moves defines a parameter, analogous to the "tolerance" parameter in classical models of spatial segregation. We show numerically and analytically that this parameter determines a true phase transition (somewhat reminiscent of phase separation in a binary mixture) between a well-mixed and a segregated state. Additionally, minority convictions segregate faster and inter-specific aversion alone may lead to a segregation threshold with similar properties. Together, our results highlight the general principle that a segregation transition is possible in absence of spatial degrees of freedom, provided that conviction-based rewiring occurs on the same time scale of popularity rewirings.

Group-based rewiring rules of binary opinion competition dynamics

The dynamics of competing opinions on networks has attracted multi-disciplinary research. Most modelling approaches assume uniform or heterogeneous behaviour among all individuals, while the role of distinctive group behaviour is rarely addressed. Here, we consider competition occurring between two opinion groups with bound rewiring rules, i.e., opinion-preferred rewiring, degree-preferred rewiring and random rewiring. When two opinions share a balanced initial proportion, opinion-preferred rewiring is superior to the other rules under low rewiring rates, and coexistence occurs under high rewiring rates. For unbalanced proportions, the best response rule for the minority/majority is unfixed, and this depends on the initial proportion and rewiring frequency. Furthermore, we find evolution processes for all competing cases belong to two categories. Evolution Category I shows an obvious correlation between opinion proportions and the density of discordant edges (connecting nodes with different opinions), and these trends can be effectively described by numerical approximations. However, for Evolution Category II, no such correlation exists for individuals or linking pairs, and an analysis of local structures reveals the emergence of large numbers of open triads with the same opinions, denoting group prevalence. This work broadens the understanding of opinion competition and inspires exploring group strategies employed in social dynamic systems.

Echo chambers and viral misinformation: Modeling fake news as complex contagion

The viral spread of digital misinformation has become so severe that the World Economic Forum considers it among the main threats to human society. This spread have been suggested to be related to the similarly problematized phenomenon of “echo chambers”, but the causal nature of this relationship has proven difficult to disentangle due to the connected nature of social media, whose causality is characterized by complexity, non-linearity and emergence. This paper uses a network simulation model to study a possible relationship between echo chambers and the viral spread of misinformation. It finds an “echo chamber effect”: the presence of an opinion and network polarized cluster of nodes in a network contributes to the diffusion of complex contagions, and there is a synergetic effect between opinion and network polarization on the virality of misinformation. The echo chambers effect likely comes from that they form the initial bandwagon for diffusion. These findings have implication for the study of the media logic of new social media.

Debunking in a world of tribes

Social media aggregate people around common interests eliciting collective framing of narratives and worldviews. However, in such a disintermediated environment misinformation is pervasive and attempts to debunk are often undertaken to contrast this trend. In this work, we examine the effectiveness of debunking on Facebook through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users usually consuming proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook US interact with specific debunking posts. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. However, both groups interact similarly with the information within their echo chamber. Then, we measure how users from both echo chambers interacted with 50,220 debunking posts accounting for both users consumption patterns and the sentiment expressed in their comments. Sentiment analysis reveals a dominant negativity in the comments to debunking posts. Furthermore, such posts remain mainly confined to the scientific echo chamber. Only few conspiracy users engage with corrections and their liking and commenting rates on conspiracy posts increases after the interaction.

Stance and influence of Twitter users regarding the Brexit referendum

Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources.

Anatomy of news consumption on Facebook

The advent of social media and microblogging platforms has radically changed the way we consume information and form opinions. In this paper, we explore the anatomy of the information space on Facebook by characterizing on a global scale the news consumption patterns of 376 million users over a time span of 6 y (January 2010 to December 2015). We find that users tend to focus on a limited set of pages, producing a sharp community structure among news outlets. We also find that the preferences of users and news providers differ. By tracking how Facebook pages "like" each other and examining their geolocation, we find that news providers are more geographically confined than users. We devise a simple model of selective exposure that reproduces the observed connectivity patterns.

Modeling confirmation bias and polarization

Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.