Limited individual attention and online virality of low-quality information

The (Mis)Information Game: A social media simulator

Given the potential negative impact reliance on misinformation can have, substantial effort has gone into understanding the factors that influence misinformation belief and propagation. However, despite the rise of social media often being cited as a fundamental driver of misinformation exposure and false beliefs, how people process misinformation on social media platforms has been under-investigated. This is partially due to a lack of adaptable and ecologically valid social media testing paradigms, resulting in an over-reliance on survey software and questionnaire-based measures. To provide researchers with a flexible tool to investigate the processing and sharing of misinformation on social media, this paper presents The Misinformation Game-an easily adaptable, open-source online testing platform that simulates key characteristics of social media. Researchers can customize posts (e.g., headlines, images), source information (e.g., handles, avatars, credibility), and engagement information (e.g., a post's number of likes and dislikes). The platform allows a range of response options for participants (like, share, dislike, flag) and supports comments. The simulator can also present posts on individual pages or in a scrollable feed, and can provide customized dynamic feedback to participants via changes to their follower count and credibility score, based on how they interact with each post. Notably, no specific programming skills are required to create studies using the simulator. Here, we outline the key features of the simulator and provide a non-technical guide for use by researchers. We also present results from two validation studies. All the source code and instructions are freely available online at https://misinfogame.com .

Reacting, Sharing, and Commenting: How Many Facebook Users Are Engaging with Posts Related to Dental Caries That Contain Misinformation?

Recent studies have been concerned about the vast amount of misinformation detected on social media that directly hampers the prevention and control of chronic diseases. Based on these facts, the aim of this study was to identify and characterize misinformation about dental caries-related content found on Facebook, regarding the predictive factors of user interaction with posts. Then, CrowdTangle retrieved 2,436 posts published in English, ordered by the total interaction of the highest users. A total of 1,936 posts were selected for inclusion and exclusion criteria to select a sample of 500 posts. Subsequently, two independent investigators characterized the posts by their time of publication, author's profile, motivation, the aim of content, content facticity, and sentiment. The statistical analysis was performed using Mann-Whitney U and χ2 tests and multiple logistic regression models to determine differences and associations between dichotomized characteristics. p values <0.05 were considered significant. In general, posts were predominantly originated from the USA (74.8%), related to business profiles (89%), presented preventive content (58.6%), and noncommercial motivation (91.6%). Furthermore, misinformation was detected in 40.8% of the posts and was positively associated with positive sentiment (OR = 3.43), business profile (OR = 2.22), and treatment of dental caries (OR = 1.60). While the total interaction was only positively associated with misinformation (OR = 1.44), the overperforming score was associated with posts from the business profile (OR = 5.67), older publications (OR = 1.57), and positive sentiment (OR = 0.66). In conclusion, misinformation was the unique predictive factor of increased user interaction with dental caries-related posts on Facebook. However, it did not predict the performance of the diffusion of posts such as business profiles, older publications, and negative/neutral sentiment. Therefore, it is essential to promote the development of specific policies toward good quality information on social media, which includes the production of adequate materials, the increase of the critical sense of consuming health content, and information filtering mediated by digital solutions.

MetaDetector: Meta Event Knowledge Transfer for Fake News Detection

The blooming of fake news on social networks has devastating impacts on society, economy, and public security. Although numerous studies are conducted for the automatic detection of fake news, the majority tend to utilize deep neural networks to learn event-specific features for superior detection performance on specific datasets. However, the trained models heavily rely on the training datasets and are infeasible to apply to upcoming events due to the discrepancy between event distributions. Inspired by domain adaptation theories, we propose an end-to-end adversarial adaptation network, dubbed as MetaDetector , to transfer meta knowledge (event-shared features) between different events. Specifically, MetaDetector pushes the feature extractor and event discriminator to eliminate event-specific features and preserve required meta knowledge by adversarial training. Furthermore, the pseudo-event discriminator is utilized to evaluate the importance of news records in historical events to obtain partial knowledge that are discriminative for detecting fake news. Under the coordinated optimization among all the submodules, MetaDetector accurately transfers the meta knowledge of historical events to the upcoming event for fact checking. We conduct extensive experiments on two real-world datasets collected from Sina Weibo and Twitter. The experimental results demonstrate that MetaDetector outperforms the state-of-the-art methods, especially when the distribution discrepancy between events is significant.

The popularity of contradictory information about COVID-19 vaccine on social media in China

To eliminate the impact of contradictory information on vaccine hesitancy on social media, this research developed a framework to compare the popularity of information expressing contradictory attitudes towards COVID-19 vaccine or vaccination, mine the similarities and differences among contradictory information's characteristics, and determine which factors influenced the popularity mostly. We called Sina Weibo API to collect data. Firstly, to extract multi-dimensional features from original tweets and quantify their popularity, content analysis, sentiment computing and k-medoids clustering were used. Statistical analysis showed that anti-vaccine tweets were more popular than pro-vaccine tweets, but not significant. Then, by visualizing the features' centrality and clustering in information-feature networks, we found that there were differences in text characteristics, information display dimension, topic, sentiment, readability, posters' characteristics of the original tweets expressing different attitudes. Finally, we employed regression models and SHapley Additive exPlanations to explore and explain the relationship between tweets' popularity and content and contextual features. Suggestions for adjusting the organizational strategy of contradictory information to control its popularity from different dimensions, such as poster's influence, activity and identity, tweets' topic, sentiment, readability were proposed, to reduce vaccine hesitancy.

Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets

We propose a simple model of an idealized online cultural market in which N items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Credibility and Involvement of Social Media in Education-Recommendations for Mitigating the Negative Effects of the Pandemic among High School Students

In the context of considerations on the potential attenuation of the negative consequences of the COVID-19 pandemic with the use of credible social media in online education during a pandemic, the subject of our own research was the fulfillment of two goals. The main research goals were to identify, categorize, and evaluate the possibilities of using social media in online education during the pandemic from the perspective of selected teachers and students from secondary schools in Slovakia. The research methods of the first phase (qualitative) of the research involved brainstorming among nine secondary school teachers. The second research phase (quantitative) used a questionnaire, which was completed by 102 high school students from all over Slovakia. The collection of both quantitative and qualitative data was used in this research. The research results revealed the most representative opinions of teachers on the current and real possibilities of engaging credible social media in online education and the views of high school students on their desired use and involvement of social media in online education. The intersection of the two findings presents a picture of the possibilities of using credible social media in online education, which can help maintain students’ interest in online education during a pandemic. Based on these findings, it can be stated that the opinions identified in the research group of teachers correspond to a large extent with the desired use of social media in education from the perspective of students. In addition, however, students would welcome more opportunities to use and engage social media in today’s online education. The result of this research is an analysis of social media patterns applied to online education, which are of greater interest to students and could act as elements for reducing the negative consequences of the COVID-19 pandemic, i.e., six forms of online education and 24 educational activities that could contribute, inter alia, to mitigating the different negative effects of the pandemic among youth generation. The findings also benefit from the presentation of many specific options and recommendations for the use of social media in online education during a pandemic.

Spanish Fact-Checking Services: An Approach to Their Business Models

The proliferation of fact-checking services is a fast-growing global phenomenon, especially in Western countries. These services are the response of journalism to disinformation, that has transformed a common internal procedure of journalistic work in the core of a business directed to the general public, also offered to the companies of mass media and social media. Literature review shows that the research on fact-checking has focused on the origin, funding, relationship with the media, procedures, and experiences related to politics and COVID-19. However, the ownership structure of the fact-checking services has been superficially analysed and the business model of these platforms has not yet been studied in detail and depth. The objective of this article is to identify and analyse the business model of the nine Spanish active fact-checking services through a documentary research of public information sources and the information that these services give about themselves. This paper explains their ownership structure and income provenance, from open information sources. The findings are that the fact-checking services that depend on media groups are no strangers to the trend of opacity usual in these groups, but in the case of fact-checking services that are born as initiatives of journalists, the trend towards transparency is, in the majority of cases, clear. However, the information provided by the Spanish fact-checking services is deficient and does not allow us to discover their business models, except in the case of Newtral and, to a certain extent, Maldita.

Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review

Social media are digitalizing massive amounts of users' cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality, and information diffusion but requires suitable interpretable frameworks. Since social media data come from users' minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users' personality traits like openness-to-experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive-, network- and computer science to understand cognitive mechanisms in both digital and real-world settings but come with limitations concerning representativeness, individual variability, and data integration. Such aspects are discussed along with the ethical implications of manipulating sociocognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy-making, education, and markets about complex cognitive trends.

The Use of Social Networking Sites and Pro-Environmental Behaviors: A Mediation and Moderation Model

Climate change poses a huge threat. Social networking sites (SNSs) have become sources of human-environment interactions and shaped the societal perception of climate change and its effect on society. This study, based on the extended parallel process model, aims to examine the effect of exposure to climate change-related information on SNSs on the pro-environmental behaviors of individuals. The study examines the mediation effect of fear of victimization from climate change between the exposure to climate change-related information on SNSs and pro-environmental behaviors, including the moderation effect of attention deficit and decision-making self-efficacy with the help of appropriate instruments. A total sample of 406 reliable questionnaires were collected from students using SNSs in China, and data were analyzed through SPSS and AMOS. Results indicate that the exposure to climate change-related information on SNSs has a direct positive effect on users' pro-environmental behaviors (β = 0.299, p < 0.01). Fear of victimization from climate change also mediates the relationship between exposure to climate change-related information on SNSs and pro-environmental behaviors (β = 0.149, SE = 0.029, p < 0.01). In addition, attention deficit moderates the relationship of exposure to climate change-related information on SNSs with fear of victimization from climate change (β = -0.090, p ≤ 0.01) and pro-environmental behaviors (β = -0.090, p ≤ 0.05). Similarly, the relationship between fear of victimization from climate change and pro-environmental behaviors is moderated by decision-making self-efficacy (β = 0.267, p ≤ 0.01). The findings offer implications for media organizations and government policy makers, who should post or spread environmental information through the most trustworthy media, with trustworthy sources, in an effective manner, and without exaggerated adverse impacts.

The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19

Leveraging social and communication technologies, we can digitally observe that the collective attention typically exhibits a heterogeneous structure. It shows that people's interests are organized in clusters around different topics, but the rising of an extraordinary emergency event, as the coronavirus disease epidemics, channels the people's attention into a more homogenized structure, shifting it as triggered by a non-random collective process. The connectedness of networked individuals, on multiple social levels, impacts on the attention, representing a tuning element of different behavioural outcomes, changing the awareness diffusion enough to produce effects on epidemics spreading. We propose a mathematical framework to model the interplay between the collective attention and the co-evolving processes of awareness diffusion, modelled as a social contagion phenomenon, and epidemic spreading on weighted multiplex networks. Our proposed modeling approach structures a systematically understanding as a social network marker of interdependent collective dynamics through the introduction of the multiplex dimension of both networked individuals and topics, quantifying the role of human-related factors, as homophily, network properties, and heterogeneity. We introduce a data-driven approach by integrating different types of data, digitally traced as user-generated data from Twitter and Google Trends, in response to an extraordinary emergency event as coronavirus disease. Our findings demonstrate how the proposed model allows us to quantify the reaction of the collective attention, proving that it can represent a social predictive marker of the awareness dynamics, unveiling the impact on epidemic spreading, for a timely crisis response planning. Simulations results shed light on the coherence between the data-driven approach and the proposed analytical model.

How weaponizing disinformation can bring down a city's power grid

Social media has made it possible to manipulate the masses via disinformation and fake news at an unprecedented scale. This is particularly alarming from a security perspective, as humans have proven to be one of the weakest links when protecting critical infrastructure in general, and the power grid in particular. Here, we consider an attack in which an adversary attempts to manipulate the behavior of energy consumers by sending fake discount notifications encouraging them to shift their consumption into the peak-demand period. Using Greater London as a case study, we show that such disinformation can indeed lead to unwitting consumers synchronizing their energy-usage patterns, and result in blackouts on a city-scale if the grid is heavily loaded. We then conduct surveys to assess the propensity of people to follow-through on such notifications and forward them to their friends. This allows us to model how the disinformation may propagate through social networks, potentially amplifying the attack impact. These findings demonstrate that in an era when disinformation can be weaponized, system vulnerabilities arise not only from the hardware and software of critical infrastructure, but also from the behavior of the consumers.

Emergent social cohesion for coping with community disruptions in disasters

Social cohesion is an important determinant of community well-being, especially in times of distress such as disasters. This study investigates the phenomena of emergent social cohesion, which is characterized by abrupt, temporary and extensive social ties with the goal of sharing and receiving information regarding a particular event influencing a community. In the context of disasters, emergent social cohesion, enabled by social media usage, could play a significant role in improving the ability of communities to cope with disruptions in recent disasters. In this study, we employed a network reticulation framework to examine the underlying mechanisms influencing emergent social cohesion on social media while communities cope with disaster-induced disruptions. We analysed neighbourhood-tagged social media data (social media data whose users are tagged by neighbourhoods) in Houston, TX, USA, during Hurricane Harvey to characterize four modalities of network reticulation (i.e. enactment, activation, reticulation and performance) giving rise to emergent social cohesion. Our results show that, unlike regular social cohesion, communication history and physical proximity do not significantly affect emergent social cohesion. The results also indicate that weak social ties play an important role in bridging different social network communities, and hence reinforce emergent social cohesion. The findings can inform public officials, emergency managers and decision-makers regarding the important role of neighbourhood-tagged social media, as a new form of community infrastructure, for improving the ability of communities to cope with disaster disruptions through enhanced emergent social cohesion.

Origin of the log-normal popularity distribution of trending memes in social networks

We study the origin of the log-normal popularity distribution of trending memes observed in many real social networks. Based on a biological analogy, we introduce a fitness of each meme, which is a natural assumption based on sociological reasons. From numerical simulations, we find that the relative popularity distribution of the trending memes becomes a log-normal distribution when the fitness of the meme increases exponentially. On the other hand, if the fitness grows slowly, then the distribution significantly deviates from the log-normal distribution. This indicates that the fast growth of fitness is the necessary condition for the trending meme. Furthermore, we also show that the popularity of the trending topic grows linearly. These results provide a clue to understand long-lasting questions, such as what causes some memes to become extremely popular and how such memes are exposed to the public much longer than others.

Community Aware Models of Meme Spreading in Micro-blog Social Networks

We propose the new models of meme spreading over social network constructed from Twitter mention relations. Our models combine two groups of diffusion factors relevant for complex contagions: network structure and social constraints. In particular, we study the effect of perceptive limitations caused by information overexposure. This effect was not yet measured in the classical models of community-aware meme spreading. Limiting our study to hashtags acting as specific, concise memes, we propose different ways of reflecting information overexposure: by limited hashtag usage or global/local increase of hashtag generation probability. Based on simulations of meme spreading, we provide quantitative comparison of our models with three other models known from literature, and additionally, with the ground truth, constructed from hashtag popularity data retrieved from Twitter. The dynamics of hashtag propagation is analyzed using frequency charts of adoption dominance and usage dominance measures. We conclude that our models are closer to real-world dynamics of hashtags for a hashtag occurrence range up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^4$$\end{document}.

Containing misinformation spreading in temporal social networks

Many researchers from a variety of fields, including computer science, network science, and mathematics, have focused on how to contain the outbreaks of Internet misinformation that threaten social systems and undermine societal health. Most research on this topic treats the connections among individuals as static, but these connections change in time, and thus social networks are also temporal networks. Currently, there is no theoretical approach to the problem of containing misinformation outbreaks in temporal networks. We thus propose a misinformation spreading model for temporal networks and describe it using a new theoretical approach. We propose a heuristic-containing (HC) strategy based on optimizing the final outbreak size that outperforms simplified strategies such as those that are random-containing and targeted-containing. We verify the effectiveness of our HC strategy on both artificial and real-world networks by performing extensive numerical simulations and theoretical analyses. We find that the HC strategy dramatically increases the outbreak threshold and decreases the final outbreak threshold.

User Interface Distribution Method Based on Pi-Calculus

The rapid growth of computing devices has led to the emergence of distributed user interfaces. A user interface is called distributed if a user can interact with it using several devices at the same time. Formal methods for designing such interfaces, in particular methods for the distribution of interface elements across multiple devices, are yet to be developed. This is the reason why every time a new application requires a distributed user interface, the latter has to be designed from scratch, rendering the entire venture economically inefficient. In order to minimize costs, unify and automate the development of distributed interfaces, we need to formulate general formal methods for designing distributed interfaces that will be independent from a particular application or device. This article paper proposes a formal distribution method based on the pi-calculus.

Infectivity enhances prediction of viral cascades in Twitter

Models of contagion dynamics, originally developed for infectious diseases, have proven relevant to the study of information, news, and political opinions in online social systems. Modelling diffusion processes and predicting viral information cascades are important problems in network science. Yet, many studies of information cascades neglect the variation in infectivity across different pieces of information. Here, we employ early-time observations of online cascades to estimate the infectivity of distinct pieces of information. Using simulations and data from real-world Twitter retweets, we demonstrate that these estimated infectivities can be used to improve predictions about the virality of an information cascade. Developing our simulations to mimic the real-world data, we consider the effect of the limited effective time for transmission of a cascade and demonstrate that a simple model of slow but non-negligible decay of the infectivity captures the essential properties of retweet distributions. These results demonstrate the interplay between the intrinsic infectivity of a tweet and the complex network environment within which it diffuses, strongly influencing the likelihood of becoming a viral cascade.

Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority

The spread of opinions, memes, diseases, and “alternative facts” in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. One feature that can change spreading dynamics substantially is heterogeneous behavior among different types of individuals in a social network. In this paper, we explore how antiestablishment nodes (e.g., hipsters) influence the spreading dynamics of two competing products. We consider a model in which spreading follows a deterministic rule for updating node states (which indicate which product has been adopted) in which an adjustable probability pHip of the nodes in a network are hipsters, who choose to adopt the product that they believe is the less popular of the two. The remaining nodes are conformists, who choose which product to adopt by considering which products their immediate neighbors have adopted. We simulate our model on both synthetic and real networks, and we show that the hipsters have a major effect on the final fraction of people who adopt each product: even when only one of the two products exists at the beginning of the simulations, a small fraction of hipsters in a network can still cause the other product to eventually become the more popular one. To account for this behavior, we construct an approximation for the steady-state adoption fractions of the products on k-regular trees in the limit of few hipsters. Additionally, our simulations demonstrate that a time delay τ in the knowledge of the product distribution in a population, as compared to immediate knowledge of product adoption among nearest neighbors, can have a large effect on the final distribution of product adoptions. Using a local-tree approximation, we derive an analytical estimate of the spreading of products and obtain good agreement if a sufficiently small fraction of the population consists of hipsters. In all networks, we find that either of the two products can become the more popular one at steady state, depending on the fraction of hipsters in the network and on the amount of delay in the knowledge of the product distribution. Our simple model and analysis may help shed light on the road to success for antiestablishment choices in elections, as such success—and qualitative differences in final outcomes between competing products, political candidates, and so on—can arise rather generically in our model from a small number of antiestablishment individuals and ordinary processes of social influence on normal individuals.

What the fake? Assessing the extent of networked political spamming and bots in the propagation of #fakenews on Twitter

Purpose

The purpose of this paper is to examine one of the largest data sets on the hashtag use of #fakenews that comprises over 14m tweets sent by more than 2.4m users.

Design/methodology/approach

Tweets referencing the hashtag (#fakenews) were collected for a period of over one year from January 3 to May 7 of 2018. Bot detection tools were employed, and the most retweeted posts, most mentions and most hashtags as well as the top 50 most active users in terms of the frequency of their tweets were analyzed.

Findings

The majority of the top 50 Twitter users are more likely to be automated bots, while certain users’ posts like that are sent by President Donald Trump dominate the most retweeted posts that always associate mainstream media with fake news. The most used words and hashtags show that major news organizations are frequently referenced with a focus on CNN that is often mentioned in negative ways.

Research limitations/implications

The research study is limited to the examination of Twitter data, while ethnographic methods like interviews or surveys are further needed to complement these findings. Though the data reported here do not prove direct effects, the implications of the research provide a vital framework for assessing and diagnosing the networked spammers and main actors that have been pivotal in shaping discourses around fake news on social media. These discourses, which are sometimes assisted by bots, can create a potential influence on audiences and their trust in mainstream media and understanding of what fake news is.

Originality/value

This paper offers results on one of the first empirical research studies on the propagation of fake news discourse on social media by shedding light on the most active Twitter users who discuss and mention the term “#fakenews” in connection to other news organizations, parties and related figures.

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

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.

Understanding and Countering Misinformation About Climate Change

While there is overwhelming scientific agreement on climate change, the public has become polarized over fundamental questions such as human-caused global warming. Communication strategies to reduce polarization rarely address the underlying cause: ideologically-driven misinformation. In order to effectively counter misinformation campaigns, scientists, communicators, and educators need to understand the arguments and techniques in climate science denial, as well as adopt evidence-based approaches to neutralizing misinforming content. This chapter reviews analyses of climate misinformation, outlining a range of denialist arguments and fallacies. Identifying and deconstructing these different types of arguments is necessary to design appropriate interventions that effectively neutralize the misinformation. This chapter also reviews research into how to counter misinformation using communication interventions such as inoculation, educational approaches such as misconception-based learning, and the interdisciplinary combination of technology and psychology known as technocognition.

The spread of low-credibility content by social bots

The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.

Social media interventions for precision public health: promises and risks

Social media data can be used with digital phenotyping tools to profile the attitudes, behaviours, and health outcomes of people. While there are a growing number of examples demonstrating the performance of digital phenotyping tools using social media data, little is known about their capacity to support the delivery of targeted and personalised behaviour change interventions to improve health. Similar tools are already used in marketing and politics, using individual profiling to manipulate purchasing and voting behaviours. The coupling of digital phenotyping tools and behaviour change interventions may play a more positive role in preventive medicine to improve health behaviours, but potential risks and unintended consequences may come from embedding behavioural interventions in social spaces.

Anatomy of an online misinformation network

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.