Trend of Narratives in the Age of Misinformation

Qualitative research on undergraduate nursing students' recognition and response to short videos' health disinformation

Background

With the popularity of the internet, short videos have become an indispensable tool to obtain health information. However, avoiding health disinformation owing to the openness of the Internet is difficult for users. Disinformation may endanger the health and lives of users.

Objective

With a focus on the process of identifying short videos' health disinformation and the factors affecting the accuracy of identification, this study aimed to investigate the identification methods, coping strategies, and the impact of short videos' health disinformation on undergraduate nursing students. The findings will provide guidance to users on obtaining high-quality and healthy information, in addition to reducing health risks.

Methods

Semi-structured in-depth interviews were conducted with 22 undergraduate nursing students in October 2022, and data were collected for collation and content analyses.

Results

The techniques used to identify short videos that include health disinformation as well as how undergraduate nursing students perceived these videos' features are among the study's findings. The failure factors in identification, coping paths, and adverse impacts of short videos on health disinformation were analyzed. The platform, the material itself, and the students' individual characteristics all have an impact on their identifying behavior.

Conclusions

Medical students continue to face many obstacles in identifying and responding to health disinformation through short videos. Preventing and stopping health disinformation not only requires individual efforts to improve health literacy and maintain rational thinking, it also requires the joint efforts of short video producers, relevant departments, and platforms.

An evaluation tool for backbone extraction techniques in weighted complex networks

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis

BACKGROUND

Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited.

OBJECTIVE

This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health.

METHODS

This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics).

RESULTS

The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2% (54/173) of the top users endorsed the corresponding diet, and 11% (19/173) claimed a health or science education, which included 1.2% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the "zone diet" network and the least prominent in the "soy-free," "vegan," "dairy-free," and "gluten-free" diet networks.

CONCLUSIONS

Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web.

IRMA: the 335-million-word Italian coRpus for studying MisinformAtion

The dissemination of false information on the internet has received considerable attention over the last decade. Misinformation often spreads faster than mainstream news, thus making manual fact checking inefficient or, at best, labor-intensive. Therefore, there is an increasing need to develop methods for automatic detection of misinformation. Although resources for creating such methods are available in English, other languages are often underrepresented in this effort. With this contribution, we present IRMA, a corpus containing over 600,000 Italian news articles (335+ million tokens) collected from 56 websites classified as 'untrustworthy' by professional factcheckers. The corpus is freely available and comprises a rich set of text- and website-level data, representing a turnkey resource to test hypotheses and develop automatic detection algorithms. It contains texts, titles, and dates (from 2004 to 2022), along with three types of semantic measures (i.e., keywords, topics at three different resolutions, and LIWC lexical features). IRMA also includes domainspecific information such as source type (e.g., political, health, conspiracy, etc.), quality, and higher-level metadata, including several metrics of website incoming traffic that allow to investigate user online behavior. IRMA constitutes the largest corpus of misinformation available today in Italian, making it a valid tool for advancing quantitative research on untrustworthy news detection and ultimately helping limit the spread of misinformation.

Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches

BACKGROUND

Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable.

OBJECTIVE

This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence.

METHODS

A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy.

RESULTS

The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences.

CONCLUSIONS

We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.

Motivation to participate in secondary science communication

The rise of social media provides convenient mechanisms for audiences to participate in secondary science communication (SSC). The present study employs the theory of consumption values and theory of planned behavior to predict audiences' SSC intentions. The results indicate that emotional value, social value, altruistic value, attitude, internal perceived behavioral control and subjective norm are significant predictors of audiences' intentions to share or to repost science content on their social media. These results suggest that the theory of consumption values, together with the theory of planned behavior, is a useful framework for understanding SSC behaviors.

Impact of COVID-19 Vaccine Misinformation on Social Media Virality: Content Analysis of Message Themes and Writing Strategies

BACKGROUND

Vaccines serve an integral role in containing pandemics, yet vaccine hesitancy is prevalent globally. One key reason for this hesitancy is the pervasiveness of misinformation on social media. Although considerable research attention has been drawn to how exposure to misinformation is closely associated with vaccine hesitancy, little scholarly attention has been given to the investigation or robust theorizing of the various content themes pertaining to antivaccine misinformation about COVID-19 and the writing strategies in which these content themes are manifested. Virality of such content on social media exhibited in the form of comments, shares, and reactions has practical implications for COVID-19 vaccine hesitancy.

OBJECTIVE

We investigated whether there were differences in the content themes and writing strategies used to disseminate antivaccine misinformation about COVID-19 and their impact on virality on social media.

METHODS

We constructed an antivaccine misinformation database from major social media platforms during September 2019-August 2021 to examine how misinformation exhibited in the form of content themes and how these themes manifested in writing were associated with virality in terms of likes, comments, and shares. Antivaccine misinformation was retrieved from two globally leading and widely cited fake news databases, COVID Global Misinformation Dashboard and International Fact-Checking Network Corona Virus Facts Alliance Database, which aim to track and debunk COVID-19 misinformation. We primarily focused on 140 Facebook posts, since most antivaccine misinformation posts on COVID-19 were found on Facebook. We then employed quantitative content analysis to examine the content themes (ie, safety concerns, conspiracy theories, efficacy concerns) and manifestation strategies of misinformation (ie, mimicking of news and scientific reports in terms of the format and language features, use of a conversational style, use of amplification) in these posts and their association with virality of misinformation in the form of likes, comments, and shares.

RESULTS

Our study revealed that safety concern was the most prominent content theme and a negative predictor of likes and shares. Regarding the writing strategies manifested in content themes, a conversational style and mimicking of news and scientific reports via the format and language features were frequently employed in COVID-19 antivaccine misinformation, with the latter being a positive predictor of likes.

CONCLUSIONS

This study contributes to a richer research-informed understanding of which concerns about content theme and manifestation strategy need to be countered on antivaccine misinformation circulating on social media so that accurate information on COVID-19 vaccines can be disseminated to the public, ultimately reducing vaccine hesitancy. The liking of COVID-19 antivaccine posts that employ language features to mimic news or scientific reports is perturbing since a large audience can be reached on social media, potentially exacerbating the spread of misinformation and hampering global efforts to combat the virus.

Transitions between polarization and radicalization in a temporal bilayer echo-chamber model

Echo chambers and polarization dynamics are, as of late, a very prominent topic in scientific communities around the world. As these phenomena directly affect our lives, seemingly more and more as our societies and communication channels evolve, it becomes ever so important for us to understand the intricacies of opinion dynamics in the modern era. Here we extend an existing echo-chamber model with activity-driven agents to a bilayer topology and study the dynamics of the polarized state as a function of interlayer couplings. Different cases of such couplings are presented: unidirectional coupling that can be reduced to a monolayer facing an external bias and symmetric and nonsymmetric couplings. We have assumed that initial conditions impose system polarization and agent opinions are different for both layers. Such a preconditioned polarized state can persist without explicit homophilic interactions provided the coupling strength between agents belonging to different layers is weak enough. For a strong unidirectional or attractive coupling between two layers a discontinuous transition to a radicalized state takes place when mean opinions in both layers are the same. When coupling constants between the layers are of different signs, the system exhibits sustained or decaying oscillations. Transitions between these states are analyzed using a mean field approximation and classified in the framework of bifurcation theory.

LOCO: The 88-million-word language of conspiracy corpus

The spread of online conspiracy theories represents a serious threat to society. To understand the content of conspiracies, here we present the language of conspiracy (LOCO) corpus. LOCO is an 88-million-token corpus composed of topic-matched conspiracy (N = 23,937) and mainstream (N = 72,806) documents harvested from 150 websites. Mimicking internet user behavior, documents were identified using Google by crossing a set of seed phrases with a set of websites. LOCO is hierarchically structured, meaning that each document is cross-nested within websites (N = 150) and topics (N = 600, on three different resolutions). A rich set of linguistic features (N = 287) and metadata includes upload date, measures of social media engagement, measures of website popularity, size, and traffic, as well as political bias and factual reporting annotations. We explored LOCO's features from different perspectives showing that documents track important societal events through time (e.g., Princess Diana's death, Sandy Hook school shooting, coronavirus outbreaks), while patterns of lexical features (e.g., deception, power, dominance) overlap with those extracted from online social media communities dedicated to conspiracy theories. By computing within-subcorpus cosine similarity, we derived a subset of the most representative conspiracy documents (N = 4,227), which, compared to other conspiracy documents, display prototypical and exaggerated conspiratorial language and are more frequently shared on Facebook. We also show that conspiracy website users navigate to websites via more direct means than mainstream users, suggesting confirmation bias. LOCO and related datasets are freely available at https://osf.io/snpcg/ .

Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data

Background: A realistic description of the social processes leading to the increasing reluctance to various forms of vaccination is a very challenging task. This is due to the complexity of the psychological and social mechanisms determining the positioning of individuals and groups against vaccination and associated activities. Understanding the role played by social media and the Internet in the current spread of the anti-vaccination (AV) movement is of crucial importance. Methods: We present novel, long-term Big Data analyses of Internet activity connected with the AV movement for such different societies as the US and Poland. The datasets we analyzed cover multiyear periods preceding the COVID-19 pandemic, documenting the behavior of vaccine related Internet activity with high temporal resolution. To understand the empirical observations, in particular the mechanism driving the peaks of AV activity, we propose an Agent Based Model (ABM) of the AV movement. The model includes the interplay between multiple driving factors: contacts with medical practitioners and public vaccination campaigns, interpersonal communication, and the influence of the infosphere (social networks, WEB pages, user comments, etc.). The model takes into account the difference between the rational approach of the pro-vaccination information providers and the largely emotional appeal of anti-vaccination propaganda. Results: The datasets studied show the presence of short-lived, high intensity activity peaks, much higher than the low activity background. The peaks are seemingly random in size and time separation. Such behavior strongly suggests a nonlinear nature for the social interactions driving the AV movement instead of the slow, gradual growth typical of linear processes. The ABM simulations reproduce the observed temporal behavior of the AV interest very closely. For a range of parameters, the simulations result in a relatively small fraction of people refusing vaccination, but a slight change in critical parameters (such as willingness to post anti-vaccination information) may lead to a catastrophic breakdown of vaccination support in the model society, due to nonlinear feedback effects. The model allows the effectiveness of strategies combating the anti-vaccination movement to be studied. An increase in intensity of standard pro-vaccination communications by government agencies and medical personnel is found to have little effect. On the other hand, focused campaigns using the Internet and social media and copying the highly emotional and narrative-focused format used by the anti-vaccination activists can diminish the AV influence. Similar effects result from censoring and taking down anti-vaccination communications by social media platforms. The benefit of such tactics might, however, be offset by their social cost, for example, the increased polarization and potential to exploit it for political goals, or increased 'persecution' and 'martyrdom' tropes.

Who Believes in COVID-19 Conspiracy Theories in Croatia? Prevalence and Predictors of Conspiracy Beliefs

The COVID-19 pandemic has given rise to numerous new conspiracy theories related to the virus. This study aimed to investigate a range of individual predictors of beliefs in COVID-19 conspiracy theories that account for sociodemographic characteristics (age, gender, education, economic standard, the importance of religion, and political self-identification), distinctive motivational orientations (social dominance and authoritarianism), relevant social attitudes (sense of political powerlessness and trust in science and scientists), and perceived personal risk (perceived risk for self and family members, the concern of being infected, and the expected influence of pandemic on the economic standard of an individual). Participants were 1,060 adults recruited from the general public of Croatia. The sample was a probabilistic quota sample with gender, age, level of education, size of the dwelling, and region of the country as predetermined quotas. The regression model explained 42.2% of the individual differences in beliefs in COVID-19 conspiracy theories. Trust in science and scientists and political powerlessness were the strongest predictors, whereas fear of being infected had the weakest contribution in explaining the variance of the criterion. Additionally, results revealed that the relation of conventionalism (as a proxy of authoritarianism) with belief in COVID-19 conspiracies was mediated by trust in science and scientists. The relation between social dominance and belief in conspiracies was also partially mediated by trust in science. The results suggest that (re)building trust in science and lowering the sense of political helplessness might help in fighting potentially harmful false beliefs about the pandemic.

Integrating Reasoned Action Approach and Message Sidedness in the Era of Misinformation: The Case of HPV Vaccination Promotion

Building upon extant research on the reasoned action approach and message sidedness, this study investigates the persuasive effects of one-sided and two-sided social media messages on the attitude about human papillomavirus (HPV) vaccination in the context of misinformation. Results of a controlled experiment (N = 251) indicated that compared to the control, one-sided messages addressing misinformation increased positive attitude about the vaccine as prior misperceptions increased. However, a backfire effect may be looming for individuals with lower prior misperceptions. Within the sidedness conditions, refutational two-sided messages were more effective in increasing cognitive attitude for individuals with lower misperceptions; whereas one-sided messages had a persuasive advantage for individuals with higher misperceptions. Theoretical and practical contributions are discussed.

Exposition to xenophobic content and support for right-wing populism: The asymmetric role of gender

This paper studies whether exposure to anti-immigrant sentiment in the online context affects the willingness to support an openly anti-immigration party, and shows how gender moderates the effect. We designed an online experiment in which participants were invited to an online forum to discuss immigration issues. We manipulate the social acceptability of xenophobic views by exposing participants to an increasing proportion of comments with anti-immigrant content. As a proxy for open support for anti-immigrant policies, we ask participants to donate to a well-known German party with a strong anti-immigration discourse: Alternative für Deutschland (Alternative for Germany). We find no evidence that exposure to increasing social acceptability of xenophobic content affected the willingness to donate. In an exploratory analysis, we find that women are particularly reluctant to donate after the anti-immigrant comments raised normative concerns. The results can shed light on the heterogeneous effect of counter-normative discourses on support for anti-immigrant parties.

News consumption patterns on Twitter: fragmentation study on the online news media network

The development of news media and social media has radically changed the way the public consumes information. This study explores the structure of online news media networks in three countries, namely Indonesia, Malaysia and Singapore, to investigate the phenomenon of fragmentation in the news consumption pattern on social media. Based on the results of the three network indicators used in this study, it can be concluded that the structure of online news media networks in Indonesia and Malaysia shows a tendency of fragmentation. In contrast, this study did not find sufficient evidence that the phenomenon of fragmentation was occurring in the Singapore media network. In-depth analysis on each formed media cluster shows that online news media in Indonesia and Malaysia tend to group based on similarity in market segments, regions or political alignments.

Factors Predicting Willingness to Share COVID-19 Misinformation

We conducted a preregistered exploratory survey to assess whether patterns of individual differences in political orientation, social dominance orientation (SDO), traditionalism, conspiracy ideation, or attitudes about science predict willingness to share different kinds of misinformation regarding the COVID-19 pandemic online. Analyses revealed two orthogonal models of individual differences predicting the willingness to share misinformation over social media platforms. Both models suggest a sizable role of different aspects of political belief, particularly SDO, in predicting tendencies to share different kinds of misinformation, predominantly conspiracy theories. Although exploratory, results from this study can contribute to the formulation of a socio-cognitive profile of individuals who act as vectors for the spread of scientific misinformation online, and can be useful for computationally modeling misinformation diffusion.

Exploring the relationship between newspaper coverage of vaccines and childhood vaccination rates in Spain

Background: Despite the effectiveness of vaccines being well established and recognized by the research community, eleven European countries have adopted mandatory vaccination programs because of vaccine hesitancy. Lack of information and fake news are considered the main reasons. The media are a powerful tool for spreading vaccine-related information. The study of media effects on vaccine uptake has received little attention in Europe.Objective: To explore the association of childhood vaccination rates in Spain with vaccine-related coverage in print media.Methods: A content analysis of newspaper coverage of vaccines was conducted. The study variables were: national vaccination rates, article publication dates, tone and main theme of the articles. We conducted a correlation analysis to assess the association between media coverage and childhood vaccine uptake.Results: While vaccine coverage with positive and neutral tones significantly increased during the study period (p < .001), the number of articles with a negative tone remained unchanged (p = .306). There was a significant and inverse correlation between negative newspaper coverage and childhood vaccine uptake (r = -.771, p < .05). During 2016 and 2017, although the media reporting declined, vaccination rates kept increasing. The most frequent themes were about the development of the Ebola vaccine, and the chickenpox and meningitis vaccine crises.Conclusions: Our findings expand the understanding of media role on vaccination and suggest that the media need to be considered as an important player during vaccination campaigns. The study points to the important educational role of the media in public health.

When Do People Verify and Share Health Rumors on Social Media? The Effects of Message Importance, Health Anxiety, and Health Literacy

This study explores the roles of perceived message importance, health anxiety, and health literacy in the relationship between message factors (message label and message valence) and behavioral intentions for rumor verification and sharing. 660 Twitter users responded to unverified information regarding the influenza vaccine. A 3 (label: none vs. news vs. rumor) × 2 (valence: positive vs. negative) online semi-experiment, with a survey to measure health anxiety and health literacy, showed the following results: First, perceived message importance mediated the relationship between message factors and behavioral intentions: only in the condition of the negative message, participants considered a news-labeled message more important than a rumor-labeled or a no-label message. Perceived message importance was associated with intentions to verify and share the message. Second, health anxiety interacted with perceived message importance only when predicting an intention to share the message. Last, healthy literacy interacted with perceived message importance when predicting intentions to both verify and share the message. The results will provide implications for health communication research and practices, especially on managing and controlling rumor dissemination on social media.

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Contemporary commentators describe the current period as “an era of fake news” in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.

•

Studies on health misinformation mainly relate to vaccine and infectious disease.

•

Findings show high prevalence and popularity of misinformation on social media.

•

Theoretical frameworks are drawn on disparate disciplinary paradigms.

•

Studies employed content analysis, social network analysis or experiments.

•

More interdisciplinary research needed to understand the susceptibility of users.

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.

PopRank: Ranking pages' impact and users' engagement on Facebook

The advent of social networks revolutionized the way people access to information sources. Understanding the complex relationship between these sources and users is crucial. 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 future posts. Pages’ impact turns out to be slightly dependent on the quality of pages’ informative content (e.g., science vs conspiracy) but independent of users’ polarization.

Influence of fake news in Twitter during the 2016 US presidential election

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co , we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.

Polarization of the vaccination debate on Facebook

BACKGROUND

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 influence on the growth of anti-vaccination groups. Recent studies w.r.t. other topics 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 information adhering to theirs system of beliefs and tend to ignore dissenting information.

OBJECTIVES

The goal was to assess whether users' attitudes are polarized on the topic of vaccination on Facebook and how this polarization develops over time.

METHODS

We perform a thorough quantitative analysis by studying the interaction of 2.6 M users with 298,018 Facebook posts over a time span of seven years and 5 months. We applied community detection algorithms to automatically detect the emergence of communities accounting for the users' activity on the pages. Also, we quantified the cohesiveness of these communities over time.

RESULTS

Our findings show that the consumption of content about vaccines is dominated by the echo chamber effect and that polarization increased over the years. Well-segregated communities emerge from the users' consumption habits i.e., the majority of users consume information in favor or against vaccines, not both.

CONCLUSION

The existence of echo chambers may explain why social-media campaigns that provide accurate information have limited reach and be effective only in sub-groups, even fomenting further opinion polarization. The introduction of dissenting information into a sub-group is disregarded and can produce a backfire effect, thus reinforcing the pre-existing opinions within the sub-group. Public health professionals should try to understand the contents of these echo chambers, for example by getting passively involved in such groups. Only then it will be possible to find effective ways of countering anti-vaccination thinking.

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.

Topic Modeling Reveals Distinct Interests within an Online Conspiracy Forum

Conspiracy theories play a troubling role in political discourse. Online forums provide a valuable window into everyday conspiracy theorizing, and can give a clue to the motivations and interests of those who post in such forums. Yet this online activity can be difficult to quantify and study. We describe a unique approach to studying online conspiracy theorists which used non-negative matrix factorization to create a topic model of authors' contributions to the main conspiracy forum on Reddit.com. This subreddit provides a large corpus of comments which spans many years and numerous authors. We show that within the forum, there are multiple sub-populations distinguishable by their loadings on different topics in the model. Further, we argue, these differences are interpretable as differences in background beliefs and motivations. The diversity of the distinct subgroups places constraints on theories of what generates conspiracy theorizing. We argue that traditional “monological” believers are only the tip of an iceberg of commenters. Neither simple irrationality nor common preoccupations can account for the observed diversity. Instead, we suggest, those who endorse conspiracies seem to be primarily brought together by epistemological concerns, and that these central concerns link an otherwise heterogenous group of individuals.

Sentiment, Contents, and Retweets: A Study of Two Vaccine-Related Twitter Datasets

INTRODUCTION

Social media platforms are important channels through which health education about the utility and safety of vaccination is conducted.

OBJECTIVE

To investigate if tweets with different sentiments toward vaccination and different contents attract different levels of Twitter users' engagement (retweets).

METHODS

A stratified random sample (N = 1425) of 142,891 #vaccine tweets (February 4, 2010, to November 10, 2016) was manually coded. All 201 tweets with 100 or more retweets from 194,259 #vaccineswork tweets (January 1, 2014, to April 30, 2015) were manually coded. Regression models were applied to identify factors associated with retweet frequency.

RESULTS

Among #vaccine tweets, provaccine tweets (adjusted prevalence ratio = 1.5836, 95% confidence interval = 1.2130-2.0713, p < 0.001) and antivaccine tweets (adjusted prevalence ratio = 4.1280, 95% confidence interval = 3.1183-5.4901, p < 0.001) had more retweets than neutral tweets. No significant differences occurred in retweet frequency for content categories among antivaccine tweets. Among 411 links in provaccine tweets, Twitter (53; 12.9%), content curator Trap.it (14; 3.4%), and the Centers for Disease Control and Prevention (8; 1.9%) ranked as the top 3 domains. Among 325 links in antivaccine tweets, social media links were common: Twitter (44; 14.9%), YouTube (25; 8.4%), and Facebook (10; 3.4%). Among highly retweeted #vaccineswork tweets, the most common theme was childhood vaccinations (40%; 81/201); 21% mentioned global vaccination improvement/efforts (42/201); 29% mentioned vaccines can prevent outbreaks and deaths (58/201).

CONCLUSION

Engaging social media key opinion leaders to facilitate health education about vaccination in their tweets may allow reaching a wider audience online.

Robust clustering of languages across Wikipedia growth

Wikipedia is the largest existing knowledge repository that is growing on a genuine crowdsourcing support. While the English Wikipedia is the most extensive and the most researched one with over 5 million articles, comparatively little is known about the behaviour and growth of the remaining 283 smaller Wikipedias, the smallest of which, Afar, has only one article. Here, we use a subset of these data, consisting of 14 962 different articles, each of which exists in 26 different languages, from Arabic to Ukrainian. We study the growth of Wikipedias in these languages over a time span of 15 years. We show that, while an average article follows a random path from one language to another, there exist six well-defined clusters of Wikipedias that share common growth patterns. The make-up of these clusters is remarkably robust against the method used for their determination, as we verify via four different clustering methods. Interestingly, the identified Wikipedia clusters have little correlation with language families and groups. Rather, the growth of Wikipedia across different languages is governed by different factors, ranging from similarities in culture to information literacy.

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.

Link prediction on Twitter

With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags.

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.

Users Polarization on Facebook and Youtube

Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view—e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media—i.e. Facebook and YouTube—over a sample of 12M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users’ commenting patterns are accurate predictors for the formation of echo-chambers.

The spreading of misinformation online

The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15--where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., "echo chambers." Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades' size.

Emotional Dynamics in the Age of Misinformation

According to the World Economic Forum, the diffusion of unsubstantiated rumors on online social media is one of the main threats for our society. The disintermediated paradigm of content production and consumption on online social media might foster the formation of homogeneous communities (echo-chambers) around specific worldviews. Such a scenario has been shown to be a vivid environment for the diffusion of false claim. Not rarely, viral phenomena trigger naive (and funny) social responses-e.g., the recent case of Jade Helm 15 where a simple military exercise turned out to be perceived as the beginning of the civil war in the US. In this work, we address the emotional dynamics of collective debates around distinct kinds of information-i.e., science and conspiracy news-and inside and across their respective polarized communities. We find that for both kinds of content the longer the discussion the more the negativity of the sentiment. We show that comments on conspiracy posts tend to be more negative than on science posts. However, the more the engagement of users, the more they tend to negative commenting (both on science and conspiracy). Finally, zooming in at the interaction among polarized communities, we find a general negative pattern. As the number of comments increases-i.e., the discussion becomes longer-the sentiment of the post is more and more negative.